diff --git a/IMPLEMENTATION_PLAN.md b/IMPLEMENTATION_PLAN.md
index 20cd592..a07ea4f 100644
--- a/IMPLEMENTATION_PLAN.md
+++ b/IMPLEMENTATION_PLAN.md
@@ -402,14 +402,14 @@ Drawer selection → update_drug_selection → app-state store → load_pathway_
 - **Checkpoint**: Cost Waterfall tab renders real data, responds to filters ✓
 
 ### 9.6 Drug Switching Sankey chart (Tab 5)
-- [ ] Create `dash_app/callbacks/sankey.py`:
+- [x] Create `dash_app/callbacks/sankey.py`:
   - Build Plotly Sankey diagram from `get_drug_transitions()` data
   - Left nodes = 1st-line drugs, middle = 2nd-line, right = 3rd-line
   - Link width = patient count, colour by drug or directorate
   - Uses `parse_pathway_drugs()` to extract drug transitions from `ids` column
-- [ ] Create figure function in `src/visualization/`
-- [ ] Wire into tab switching
-- **Checkpoint**: Sankey tab renders real drug transition flows
+- [x] Create figure function in `src/visualization/`
+- [x] Wire into tab switching
+- **Checkpoint**: Sankey tab renders real drug transition flows ✓
 
 ### 9.7 Dosing Interval Comparison chart (Tab 6)
 - [ ] Create `dash_app/callbacks/dosing.py`:
diff --git a/dash_app/callbacks/chart.py b/dash_app/callbacks/chart.py
index 5d6a088..1b56d59 100644
--- a/dash_app/callbacks/chart.py
+++ b/dash_app/callbacks/chart.py
@@ -158,6 +158,31 @@ def _render_cost_waterfall(app_state, title):
     return create_cost_waterfall_figure(data, title)
 
 
+def _render_sankey(app_state, title):
+    """Build the Sankey diagram from current filter state."""
+    from dash_app.data.queries import get_drug_transitions
+    from visualization.plotly_generator import create_sankey_figure
+
+    filter_id = (app_state or {}).get("date_filter_id", "all_6mo")
+    chart_type = (app_state or {}).get("chart_type", "directory")
+
+    selected_dirs = (app_state or {}).get("selected_directorates") or []
+    selected_trusts = (app_state or {}).get("selected_trusts") or []
+    directory = selected_dirs[0] if len(selected_dirs) == 1 else None
+    trust = selected_trusts[0] if len(selected_trusts) == 1 else None
+
+    try:
+        data = get_drug_transitions(filter_id, chart_type, directory, trust)
+    except Exception:
+        log.exception("Failed to load drug transition data")
+        return _empty_figure("Failed to load drug transition data.")
+
+    if not data.get("nodes") or not data.get("links"):
+        return _empty_figure("No drug switching data available.\nTry adjusting your filters.")
+
+    return create_sankey_figure(data, title)
+
+
 def register_chart_callbacks(app):
     """Register tab switching, pathway data loading, and chart rendering callbacks."""
 
@@ -280,6 +305,9 @@ def register_chart_callbacks(app):
         elif active_tab == "cost-waterfall":
             fig = _render_cost_waterfall(app_state, title)
 
+        elif active_tab == "sankey":
+            fig = _render_sankey(app_state, title)
+
         else:
             # Placeholder for charts not yet implemented
             tab_label = dict(TAB_DEFINITIONS).get(active_tab, active_tab)
diff --git a/src/visualization/plotly_generator.py b/src/visualization/plotly_generator.py
index 8be52cd..dee7d8d 100644
--- a/src/visualization/plotly_generator.py
+++ b/src/visualization/plotly_generator.py
@@ -703,6 +703,137 @@ def create_cost_waterfall_figure(
     return fig
 
 
+def create_sankey_figure(
+    data: dict,
+    title: str = "",
+) -> go.Figure:
+    """Create Sankey diagram showing drug switching flows between treatment lines.
+
+    Args:
+        data: Dict from get_drug_transitions() with keys:
+              nodes: [{name}] — drug names with ordinal suffixes (e.g., "ADALIMUMAB (1st)")
+              links: [{source_idx, target_idx, patients}] — transitions between drugs
+        title: Chart title suffix (filter description).
+
+    Returns:
+        Plotly Figure with Sankey diagram.
+    """
+    import re
+
+    nodes = data.get("nodes", [])
+    links = data.get("links", [])
+
+    if not nodes or not links:
+        return go.Figure()
+
+    # NHS colour palette — one colour per unique base drug name
+    nhs_colours = [
+        "#005EB8", "#003087", "#41B6E6", "#0066CC", "#1E88E5",
+        "#4FC3F7", "#009639", "#ED8B00", "#768692", "#AE2573",
+        "#8A1538", "#330072", "#DA291C", "#00A499", "#425563",
+    ]
+
+    # Extract base drug name (strip ordinal suffix) for colour consistency
+    def base_drug(name: str) -> str:
+        return re.sub(r"\s*\(\d+(?:st|nd|rd|th)\)\s*$", "", name)
+
+    unique_bases = []
+    for n in nodes:
+        b = base_drug(n["name"])
+        if b not in unique_bases:
+            unique_bases.append(b)
+    base_colour_map = {b: nhs_colours[i % len(nhs_colours)] for i, b in enumerate(unique_bases)}
+
+    # Node colours — same drug gets same colour regardless of treatment line
+    node_colours = [base_colour_map[base_drug(n["name"])] for n in nodes]
+
+    # Node labels — format nicely
+    node_labels = [n["name"] for n in nodes]
+
+    # Link colours — use source node colour at 40% opacity for visual clarity
+    def hex_to_rgba(hex_colour: str, alpha: float) -> str:
+        h = hex_colour.lstrip("#")
+        r, g, b = int(h[0:2], 16), int(h[2:4], 16), int(h[4:6], 16)
+        return f"rgba({r},{g},{b},{alpha})"
+
+    link_colours = [
+        hex_to_rgba(node_colours[link["source_idx"]], 0.35)
+        for link in links
+    ]
+
+    # Build hover text for links
+    link_hovers = [
+        f"{node_labels[link['source_idx']]} → {node_labels[link['target_idx']]}"
+        f"<br>Patients: {link['patients']:,}"
+        for link in links
+    ]
+
+    # Compute total patients per node for node hover
+    node_patients = [0] * len(nodes)
+    for link in links:
+        node_patients[link["source_idx"]] += link["patients"]
+    # For terminal nodes (no outgoing), use incoming total
+    node_incoming = [0] * len(nodes)
+    for link in links:
+        node_incoming[link["target_idx"]] += link["patients"]
+    node_hover = []
+    for i, n in enumerate(nodes):
+        out_p = node_patients[i]
+        in_p = node_incoming[i]
+        total = max(out_p, in_p)
+        node_hover.append(f"<b>{n['name']}</b><br>Patients: {total:,}")
+
+    fig = go.Figure(
+        go.Sankey(
+            arrangement="snap",
+            node=dict(
+                pad=20,
+                thickness=25,
+                line=dict(color="#FFFFFF", width=1),
+                label=node_labels,
+                color=node_colours,
+                customdata=node_hover,
+                hovertemplate="%{customdata}<extra></extra>",
+            ),
+            link=dict(
+                source=[link["source_idx"] for link in links],
+                target=[link["target_idx"] for link in links],
+                value=[link["patients"] for link in links],
+                color=link_colours,
+                customdata=link_hovers,
+                hovertemplate="%{customdata}<extra></extra>",
+            ),
+        )
+    )
+
+    chart_title = "Drug Switching Flows"
+    if title:
+        chart_title = f"{chart_title} — {title}"
+
+    fig.update_layout(
+        title=dict(
+            text=chart_title,
+            font=dict(
+                family="Source Sans 3, system-ui, sans-serif",
+                size=18,
+                color="#003087",
+            ),
+            x=0.5,
+            xanchor="center",
+        ),
+        font=dict(
+            family="Source Sans 3, system-ui, sans-serif",
+            size=12,
+        ),
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        margin=dict(t=60, l=30, r=30, b=30),
+        height=max(500, len(unique_bases) * 35 + 200),
+    )
+
+    return fig
+
+
 def save_figure_html(
     fig: go.Figure, save_dir: str, title: str, open_browser: bool = False
 ) -> str: