This study develops a robust forecasting framework to evaluate human capital readiness in the tourism sector, focusing on Asian economies using education-related indicators from the Travel and Tourism Development Index (TTDI). A Composite Readiness Index (CRI) was constructed through feature engineering, including z-score normalization, growth indices, min–max scaling, lagged features, rolling means, and first differences, to capture both relative performance and temporal persistence. Five machine learning models were trained using a time-aware split (2000–2021 for training, 2024 for testing), with Gradient Boosting, Random Forest, Ridge regression, ElasticNet, and Support Vector Regression (SVR) compared. While ensemble models achieved near-perfect R² scores (0.997 and 0.995), their accuracy reflects overfitting and limited interpretability. Ridge regression produced a strong yet credible R² of 0.981, offering transparency through interpretable coefficients and robust generalization. Results highlight the persistence of workforce readiness, with past performance strongly shaping future outcomes, and emphasize the need for sustained investment in education and training to enhance tourism competitiveness in post-pandemic Asia.
Tourism Competitiveness; Human Capital Readiness; Composite Readiness Index (CRI); Machine Learning Forecasting; Ridge Regression; Asia-Pacific