Agriculture occupies a third of Earth's surface and is vital for food production. Rice, grown from paddy seeds, feeds nearly half the global population. To meet rising food demands, this study aims to enhance rice production using Machine Learning (ML) to predict factors affecting paddy growth. A Hybrid ML Model with Combined Wrapper Feature Selection (HMLCWFS) was developed to address challenges like overfitting and computational costs. Five Feature Selection (FS) methods—Backward Elimination, Stepwise Forward Selection, Feature Importance, Exhaustive FS, and Gradient Boosting—were applied. Selected features were merged using Poincaré’s formula to form a refined dataset. ML models such as Decision Tree, Random Forest, SVM, KNN, and Naive Bayes were trained and tested. The model not only forecasts yield but also recommends paddy varieties based on farmers' preferences. Results show that combined FS techniques effectively identify key factors for improving paddy productivity.