This study aims to enhance rice production prediction through a comparative analysis of machine learning models utilizing climate variables. Eight models were assessed on a predetermined dataset, with Support Vector Regression (SVR) emerging as the top performer. Following the identification of significant climate variables influencing rice production, the models underwent evaluation using two hyperparameter approaches: random search and manual tuning. SVR outperformed other models, achieving impressive metrics with MAE 0.180, MSE 0.186, RMSE 0.431, and an exceptionally low MAPE of 0.020. Key factors influencing rice production included productivity and area, along with humidity, rainfall, temperature, wind velocity, and sunshine duration. Favorable conditions for rice output encompassed low humidity, moderate rainfall, increased wind speed, and prolonged sunshine, while rainfall and temperature exhibited minimal impact. The success of random search emphasizes the importance of effective hyperparameter tuning. This research provides valuable insights for enhancing rice production prediction.