Tourism is a pillar industry of the national economy and can reflect the overall economic development level of Sanya. Using the tourism data of Sanya from 2012 to 2023, we applied principal component analysis to extract two principal components from 11 indicators affecting the city’s tourism revenue. Python was employed to develop regression models and GM(1,1) models for predicting Sanya’s tourism revenue. The results show that in recent years, Sanya’s tourism revenue has been on the rise. The number of domestic tourists is the main factor affecting Sanya’s tourism revenue. Sanya’s tourism revenue is mainly influenced by domestic tourism revenue, the number of domestic tourists, the number of domestic overnight tourists in Sanya, the number of inbound overnight tourists in Sanya, and tourism foreign exchange earnings, etc. However, tourism hotels and other factors have no significant impact on Sanya’s tourism revenue. Based on the root mean square error (RMSE) criterion, regression models exhibit superior predictive performance compared to the GM(1,1) model in forecasting Sanya’s tourism revenue.