<p>This study proposes a framework for optimizing biodiesel production from high free fatty acid milk scum oil using deep learning models trained on high-fidelity synthetic process data. The approach demonstrates that deep learning can accurately predict complex process outcomes while maintaining interpretability through systematic ablation analysis. A two-step biodiesel production process is mathematically modelled to generate a comprehensive synthetic dataset. A multilayer perceptron (MLP) model is trained on this dataset to predict biodiesel yield and intermediate FFA content, and an ablation study examines the contribution of key input features and network layers to performance. The methodology is implemented in Python with code provided for reproducibility. The MLP model achieves high accuracy in predicting biodiesel yield and FFA reduction from synthetic data, showing that deep learning can capture complex nonlinear relationships. Ablation results highlight critical process parameters (e.g., reaction temperature, catalyst concentration) and specific neural network layers that significantly affect prediction accuracy. These insights enhance the interpretability and trustworthiness of the model in a chemical engineering context. This work is the first to combine high-fidelity synthetic data generation, deep learning modelling, and systematic ablation analysis for biodiesel optimization from high-FFA milk scum oil. It offers both strong predictive capability and transparent interpretability for process optimization. The framework provides a cost-effective, safe alternative to extensive lab trials, giving engineers a computational tool to design and refine biorefineries while promoting waste-to-fuel enhancement in India.</p>