Abstract: Rice is a vital staple crop and an essential component of the diet in many Asian nations. Over the years challenges have arisen in rice farming due to the vulnerability of rice crops to attacks by more than 100 types of insects which can cause significant yield losses. This research focuses on predicting rice bug infestations in rice fields using data mining and machine learning techniques. Several machine learning algorithms including Decision Tree, Optimizable Discriminant, Naïve Bayes, Support Vector Machine, K-Nearest Neighbors, Ensemble, and Neural Network, were employed to analyzed patterns and build predictive models for rice pest infestations. The dataset spans from 2005 to 2019 and includes historical data, field observations, and interviews with agriculturists and farmers. The study aims to enhance understanding of the factors influencing rice bug infestations and to develop timely alert systems to minimize potential crop losses. Model performance was improved through the fine-tuning of hyperparameters, with training and evaluation conducted using an 80:20 data split and 10-fold cross-validation. The KNN and Ensemble models achieved the highest accuracy rates at 98.3%. These findings provide valuable insights for agricultural institutions and farmers in selected municipalities of Region IVA and may also be adapted for other crops with similar pest challenges using comparable parameters.