Abstract
Sustainable tourism development demands informed data-driven planning to balance economic growth with environmental and social responsibility. This study explores the vital role of statistical modelling in achieving such a balance, with a focus on core methods such as regression analysis, time series forecasting, and principal component analysis (PCA). Regression models offer insights into tourism demand and regional development, whereas time-series techniques support efficient forecasting and planning. PCA enables dimensionality reduction and identification of key sustainability indicators. Case studies from diverse regions, including Vietnam, Romania, and Zimbabwe, highlight how statistical methods guide strategic decision making, especially in data-constrained environments. This study also underscores the integration of machine learning, hybrid models, and big data analytics as an evolving frontier in tourism planning. Ultimately, statistical modelling has emerged as an indispensable tool for developing adaptive, localized, and effective strategies to foster long-term sustainability in the tourism sector.