Unconventional production techniques significantly affect reaction parameters and sustainable biodiesel production. Achieving a sustainable and resilient energy future involves feedstock selection, oil extraction, and biodiesel production. A comprehensive analysis of the several phases is necessary to lower the overall economics of biodiesel production. Mechanical pressing, solvent extraction, and boiling are common oil extraction industrial methods, each with notable benefits and drawbacks. Key criteria for selecting extraction methods include seed type, oil yield, product quality, economic viability, environmental impact, scale of production, energy efficiency, speed, by-product use, and raw material availability. The triglycerides in extracted oil and fats are transformed into biodiesel using transesterification, hydroprocessing, pyrolysis, catalytic and reactive distillation, micro-emulsion, supercritical fluid method, etc. The advantages and limitations of each method were discussed. Holistic assessment on feedstock availability and suitability, reaction kinetics, conversion efficiency, catalyst selection, glycerol separation, scale-up potential, environmental impact, and biodiesel purity ensures easy selection of cost-effective biodiesel production methods that offer high yield with minimized energy consumption and waste. Transesterification is a widely practised biodiesel production technique in literature for high-quality biodiesel production from diverse feedstock. Transesterification factors such as methanol-to-oil molar ratio, catalyst loading, reaction time and temperature, and stirring speed influence the biodiesel conversion efficiency, quality, and cost. The methods practised for experimentation and analysis are discussed, and their advantages and limitations are highlighted. Design of experiments (DOE) helps reduce overall experiments and draw meaningful conclusions with a systematic study of parametric (both individual and interaction factors) analysis. Artificial intelligence (AI) tools are applied to model and analyse input-output (transesterification parameters-biodiesel yield, engine parameters-performance, and emission characteristics) relationships and optimize them by leveraging computers based on human intelligence. The biodiesel research experimentation, modelling, analysis, and optimization framework were established using DOE and AI tools.