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The thermochemical conversion of biomass is a promising technology due to its cost-effectiveness and feedstock flexibility, with pyrolysis being a particularly noteworthy method for its diverse product range. Despite the potential of pyrolysis, commercialization remains elusive, and there is a growing need to fully understand its dynamics to facilitate process scaling up. However, waste biomass pyrolysis is complex, time-consuming, and capital-intensive. Machine Learning (ML) has emerged as a possible means of supporting and accelerating pyrolysis research despite these challenges. This study provides a comprehensive overview of the use of ML in pyrolysis, from biorefinery to end-of-life product management. In addition, the success of ML in process optimization and control, predicting product yield, real-time monitoring, life-cycle assessment (LCA), and techno-economic analysis (TEA) during biomass pyrolysis …