Global transition toward sustainable and efficient energy storage has intensified research into supercapacitors, valued for their rapid charge-discharge capability, high power density, and long operational lifespan. Among carbonaceous materials, biomass-derived biochar has emerged as a low-cost, renewable, and structurally tunable precursor for electrode design. This review provides a structured and critical synthesis of biochar-based supercapacitor strategies, systematically categorizing heteroatom doping, nanocomposite engineering, and hybrid architecture within an integrated electrochemical performance framework. Unlike existing literature that predominantly summarizes modification approaches, this review emphasizes cross-study comparability, mechanistic attribution, and device-level relevance to clarify transferable design principles. A consolidated analysis of recent machine learning (ML) applications reveals a paradigm shift from empirical optimization to data-driven predictive design. Benchmarking against reported datasets, descriptors, and model architectures, we confirm that tree-based ensemble and neural-network models (e.g., XGBoost, LightGBM, ANN) consistently achieve high predictive accuracy (R² > 0.9), while also identifying current limitations in data standardization and descriptor harmonization. The review critically evaluates how ML can move beyond correlation towards interpretable and transferable optimization under realistic device constraints. Finally, a converging research roadmap is proposed, prioritizing standardized reporting, device-level benchmarking, and constraint-aware ML integration to accelerate scalable implementation. By integrating materials engineering, data-driven modeling, and practical deployment considerations, this review establishes a comprehensive framework for advancing sustainable biochar-based supercapacitors toward real-world energy storage applications. • Biochar provides a green, low-cost carbon source for supercapacitor electrodes • Heteroatom-doped and nanocomposite biochar increase capacitance and conductivity • ML models can efficiently forecast supercapacitor performance • XGBoost and SHAP can elucidate chemistry, porosity and speciation role in biochar performance
Sajjad et al. (Tue,) studied this question.