Local artisan products are essential in preserving cultural heritage and supporting community livelihoods. They contribute to e-commerce initiatives aiming to address sustainability through ethical consumption, reduced environmental impact, and inclusive economic growth. This paper examines the roles of various factors in online shopping intention of local artisan products. The data were collected through surveys and analyzed using various machine learning models such as Decision Trees, Gradient Boosting, Random Forests, XGBoost, K-Nearest Neighbors, and Support Vector Machines to predict consumer behavior and market trends. Results show that support vector machine outperforms the rest of the models in predicting online shopping intention of local artisan products. The findings provide insights into e-commerce strategies for sustainable economic development and cultural preservation in the Philippines.