Conventional experiments play a crucial role in determining the thresholds at which biomass ash addition begins to adversely affect the mechanical properties of composites. These experiments are often costly and limited to the specific biomass ash analysed. In contrast, Machine Learning (ML) models provide a robust and scalable alternative. First, they learn how the mechanical properties of materials are influenced by the various oxides present in biomass. Then, they generate models that capture the interactions between these oxides and their effects on the mechanical properties of composites, specifically Aluminium Metal Matrix Composites (AMMCs) in this case. This study investigates the influence of biomass ash on AMMCs using a dataset of 317 observations and 35 variables. Machine Learning (ML) models predict mechanical properties based on oxide compositions. Lasso regression, Least Squares Boosting, and Neural Networks were evaluated for accuracy, precision, and robustness at varying performance levels (high, moderate, and low), with the Least Squares Boosting model demonstrating superior predictive performance. Interpretability tools, Partial Dependency Plots and Response Surface Methodology, show that the oxide interactions of SiO2, MgO & CaO enhanced the hardness, Al2O3 and Fe2O3 improved tensile strength, while SiO2 & Al2O3 improved elongation.