Optimization of biodiesel properties using <scp>Free Fatty Acid</scp> ‐based blending, <scp>Artificial and Machine Learning</scp> modelling, and advanced production technologies: A comprehensive review

Abstract This study presents a novel approach for optimizing biodiesel properties through the strategic blending of feedstocks with different free fatty acid profiles and the application of advanced production techniques. The unique contribution lies in the integration of AI and machine learning models with FFA‐based blending strategies to develop predictive frameworks for tailoring biodiesel blends to specific engine and climatic requirements. Strategic blending of biodiesels from feedstocks rich in specific FFAs enhances key physicochemical properties: saturated fatty acids such as palmitic (C16:0), stearic (C18:0), and lauric (C12:0) improve oxidative stability, flash point, and cetane number, while unsaturated fatty acids like oleic (C18:1) and linoleic (C18:2) reduce viscosity and enhance cold flow characteristics. AI/ML techniques, including SVM, ANN, XGBoost, CatBoost, and deep neural networks, enable high‐precision prediction of FFA profiles and physicochemical properties such as cetane number, viscosity, density, cold flow behavior, and oxidative stability. In parallel, advanced production methods such as ultrasound‐assisted transesterification, supercritical fluid processing, and nano‐biocatalysts improve the biodiesel yield and physicochemical properties of the feedstock. The synergistic integration of predictive AI/ML models with innovative blending and catalytic techniques provides a comprehensive, data‐driven framework for producing high‐quality biodiesel that meets global fuel standards and promotes sustainable adoption in the transportation sector.