The accurate prediction of crude oil production is crucial for effective management of oil reservoir operations. This paper leverages recent advancements in machine learning techniques and metaheuristic optimization algorithms, specifically deep learning (DL) and metaheuristic (MH) approaches, to construct a robust and efficient oil production prediction model. Real-world datasets from two diverse countries, Yemen and China, are employed in model development. The study focuses on optimizing a multilayer perceptron (MLP) using the Runge–Kutta optimizer (RUN). The primary goal is to enhance the MLP parameters through the application of the RUN algorithm. Rigorous evaluation experiments gauge the efficacy of the resulting prediction model (RUN-MLP), demonstrating impressive performance across three widely recognized evaluation metrics: root-mean-square error (RMSE), mean absolute error (MAE), and coefficient of determination (