In the past few years, high consumption of fossil fuels has led to a serious energy crisis due to increased demand of energy because of industrialization and modernization. It resulted in environmental concerns due to greenhouse gas emission with energy security issues. These fossil fuel resources have been getting threshold and exhaust as well as depletion. These issues have prompted the research to find out the alternative energy resources which are technically feasible, environmentally acceptable and economical competitive. Biofuels as an alternative to the petrodiesel are originated by inorganic ingredients instead of fossil fuels. Biodiesel production is abruptly growing worldwide due to its characteristic, such biodegradable, non-toxic, and sustainable energy. Biofuel production cost is a challenging part for its implementation that can be possibly overcome by utilization of waste feedstock as well as costly catalyst replace by available and waste material. The importance of energy systems and its role in economics and politics is not hidden for anyone.

This issue is not only important for the advanced industrialized countries, which are major energy consumers, but is also important for oil-rich countries. In addition to the nature of these fuels which contains polluting substances, the issue of their ending up has aggravated the growing concern. Accordingly, the need for handling, modelling, decision making and future forecasting for biofuels can be one of the main challenges for scientists. Recently, machine learning and deep learning techniques have been popular in modeling, optimizing and handling the biodiesel production, consumption and its environmental impacts. The main aim of this course is to evaluate the Machine Learning techniques developed for handling biofuels production, consumption and environmental impacts, both for modeling and optimization purposes. This will help for sustainable biofuel production for the future generations