Thermochemical conversions of nonedible biomass into energy are promising alternatives for ensuring a sustainable energy

society. However, determining the optimum design and operating conditions of the processes remains a major challenge due

to the laborious and costly experimental methods. Machine learning techniques are cost-eff ective and non-time consuming

and have been widely utilized in thermochemical conversion process modelling with robust and accurate results and solutions.

Nonetheless, no standard method has been proposed for applying ML models to biomass thermochemical processes.

Consequently, the general development procedure for ML models with high accuracy and robustness remains unclear. This

review provides a comprehensive review of machine learning techniques for predicting biofuel yield and composition. It is

recommended that quality datasets be ensured to enable the development of more robust machine learning-aided models for

practical engineering applications. Finally, solutions to the identifi ed challenges and prospective future research directions

on machine learning-based biomass thermochemical conversion processes are recommended to accelerate the optimization

and large-scale deployment of these processes.