Biomass pyrolysis is a complex thermochemical conversion process influenced by multiple interdependent parameters such as temperature, heating rate, pyrolysis time, composition of feedstock, and type of catalyst. Traditional modeling approaches often struggle to capture these nonlinear interactions effectively. This comprehensive review explores the integration of machine learning (ML) techniques in biomass pyrolysis, highlighting their growing role in optimizing process conditions, predicting product yields, and accelerating research and development. ML algorithms, ranging from decision trees, support vector machines, and neural networks to ensemble and hybrid models, offer promising alternatives by learning patterns from experimental and literature datasets. The review evaluates recent studies on ML applications for predicting bio-oil, biochar, and syngas yields, analyzing feature importance, and model performance metrics like R2, RMSE, and MAE. Finally, future trends are suggested, including model interpretability, integration with kinetic modeling, and the use of real-time data.