Khadeejah James AuduMarshal BenjaminUmaru MohammedYusuph Amuda Yahaya2025-04-232024-06-1610.Audu, K. J., Marshal, B., Mohammed, U. & Yahaya, Y. A. (2024). Artificial Neural Network Approach for the Resolution of First-Order Ordinary Differential Equations. Malaysian Journal of Science and Advanced Technology, 4(3) 210-216.https://mjsat.com.my/http://repository.futminna.edu.ng:4000/handle/123456789/900This research focuses on using Artificial Neural Networks (ANNs) to solve first-order Ordinary Differential Equations (ODEs). Traditional numerical methods often struggle with complex or nonlinear equations, especially in terms of computational efficiency. To tackle this, the study explores how ANNs—known for their ability to approximate complex functions—can be applied as an alternative solution technique. The approach involves training an ANN to learn the solution to different types of first-order ODEs. Through a series of numerical experiments, the study evaluates how well the ANN performs in terms of accuracy, convergence, and computational speed. The findings show that ANNs can be a reliable and efficient method for solving first-order ODEs, with potential applications in various scientific and engineering problems.Ordinary Differential Equations (ODEs) play a crucial role in various scientific and professional domains for modeling dynamic systems and their behaviors. While traditional numerical methods are widely used for approximating ODE solutions, they often face challenges with complex or nonlinear systems, leading to high computational costs. This study aims to address these challenges by proposing an artificial neural network (ANN)- based approach for solving first-order ODEs. Through the introduction of the ANN technique and exploration of its practical applications, we conduct numerical experiments on diverse first-order ODEs to evaluate the convergence rate and computational efficiency of the ANN. Our results from comprehensive numerical tests demonstrate the efficacy of the ANN-generated responses, confirming its reliability and potential for various applications in solving first-order ODEs with improved efficiency and accuracy.enFirst-Oder ODEArtificial Neural NetworkComputational EfficiencyNumerical TechniqueConvergence AnalysisArticle