A Machine Learning Approach to Rainfall Occurrence Prediction: A Case Study of Akure South Ayoola Emmanuel Awode 1* 1 Department of Civil and Environmental Engineering, School of Engineering and Engineering Technology, The Federal University of Technology, Akure, Nigeria *Correspondence: awodeae@futa.edu.ng ORCID ID: 0000-0003-1767-854X Keywords: rainfall, classification, climate change, predictive modeling, machine learning ABSTRACT Due to recent climate change and volatility, it is challenging to predict rainfall occurrences accurately. The usefulness of classification systems for predicting rainfall has continued to increase. This study uses different categorization algorithms for rainfall occurrence prediction in the Akure South region of Nigeria. The machine learning algorithms used include Logistic Regression, Linear Discriminant Analysis, K-Neighbors, Decision-Tree, Gaussian Naive Bayes, Support Vector, Ada-Boost, and Gradient Boosting classifiers. The dataset used was sourced from the National Aeronautics and Space Administration (NASA) Langley research center (LARC) Prediction of Worldwide Energy Resource (POWER) Project funded through the NASA Earth Science/Applied Science Program. The dataset included a range of climatic variables (precipitation, surface pressure, specific humidity, and wind speed) data and spanned the years 1981 through 2022. Using a 75:25 ratio of training to testing data, the performances of the algorithms were assessed based on their accuracy, precision, recall, and f1 score. The Decision-Tree classifier fared best, with the highest values for accuracy, precision, recall, and f1-score. 1.0 INTRODUCTION Rainfall is a significant meteorological factor in the management of water resource (Segond et al., 2007). With the aid of precise precipitation forecasts, decisions on agriculture, traffic management, and the control of natural disasters like droughts and floods can all be improved. A new phase of intervention is anticipated to be introduced to the impacted sectors confronted with the adverse predispositions to rainfall extremes by accurate and timely rainfall forecast. These crucial industries, which are significantly impacted by rainfall, include but are not