Abstract <p>Biomass pyrolysis plays a crucial role in advancing sustainable energy solutions because of its carbon–neutral potential. However, kinetic analysis of this process particularly for lignocellulosic and algal feedstocks remains challenging due to methodological and modelling complexities. This review synthesizes recent advances in kinetic modelling and thermogravimetric analysis (TGA) for both lignocellulosic and algal biomasses, emphasizing the critical differences in their thermal decomposition behaviors and the resulting challenges in determining kinetic parameters. We systematically evaluate state-of-the-art sample pre-treatment techniques and experimental protocols essential for reliable TGA-based kinetic studies. Furthermore, we provide a detailed, stepwise comparison of both classical and emerging approaches for calculating the kinetic triplet: activation energy, reaction model function, and pre-exponential factor. Special attention is given to the limitations of traditional isoconversion and model-fitting methods when applied to complex, multi-component biomass systems, alongside the increasing adoption of multi-step kinetic schemes and advanced numerical optimization techniques that address these issues. Additionally, this review critically examines the integration of artificial intelligence, particularly artificial neural networks in predicting kinetic parameters and modelling complex pyrolysis reactions, presenting current achievements as well as future prospects. By consolidating methodological advances and identifying persistent gaps, this review offers a comprehensive resource for researchers aiming to improve the accuracy and applicability of kinetic analysis methods in the thermochemical conversion of diverse biomass feedstocks, ultimately supporting the development of more efficient bioenergy technologies.</p>