Accurate tourism demand forecasting is essential for effective destination management, hotel revenue planning, transportation optimization, and policy-making. Traditional forecasting techniques—such as ARIMA, exponential smoothing, and regression models—often fail to capture nonlinear patterns and complex external influences. Machine learning (ML) provides powerful alternatives capable of processing large datasets, identifying hidden relationships, and improving prediction accuracy. This article explores the role of ML in tourism demand forecasting, reviewing common algorithms, data sources, model evaluation techniques, and practical applications. Conceptual diagrams and hypothetical case examples illustrate ML-enhanced forecasting scenarios. Findings indicate that ML models significantly outperform traditional methods, especially when incorporating real-time data streams such as social media, weather, mobility, and online search trends. Challenges include data quality issues, overfitting, interpretability limitations, and the need for AI governance.