Infrastructure, Governance, and Price Stability as Binding Constraints on Inbound Tourism to India

India’s inbound tourism potential could not be fully realised even though it has rich heritage resources, expanding air networks, and sustained policy attention. To identify the key determinants of foreign tourism demand, this study applies an interpretable machine learning framework using a panel data of 61 source countries to India over 2002–2024. Using a gravity-based tourism demand model, the analysis estimates an XGBoost regression for predicting tourism demand based on origin-country income, India’s hotel capacity, domestic and international aircraft movements, UNESCO heritage sites, mega-events, inflation, and governance indicators. Accumulated Local Effects (ALE) and SHAP values were used as interpretable tools. Results show that source-country income and air connectivity are the most influential drivers of arrivals, while heritage sites and hotel rooms display clear saturation and diminishing returns, and governance and inflation exert only mild or non-linear effects. Mega-events provide small and inconsistent short-run gains without strong persistence. The findings indicate that India’s future tourism gains lie less in further capacity expansion and more in strengthening air connectivity, strategically targeting emerging middle-income markets, and upgrading quality, governance, and price stability to convert existing assets into sustained, spatially dispersed arrivals.