Sajjad, FatimaFatimaSajjadIftikhar, RashidRashidIftikharInam, Muhammad AliMuhammad AliInamZadran, SaifullahSaifullahZadranHaider, SalmanSalmanHaiderSaleem, SaharSaharSaleemNadeem, HumayunHumayunNadeemAlim-un-Nisa2026-04-152026-04-152026-03-31Next Materials 11: 101971 (2026)https://hdl.handle.net/11420/62667Global 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.en2949-8228Next materials2026Elsevierhttps://creativecommons.org/licenses/by/4.0/Carbon electrode modificationElectrochemical energy storageHeteroatom dopingNanocomposite engineeringPredictive modelingTechnology::621: Applied Physics::621.3: Electrical Engineering, Electronic EngineeringNatural Sciences and Mathematics::541: Physical; Theoretical::541.3: Physical ChemistryMachine learning-driven optimization of biochar-based supercapacitors for sustainable energy storage: mechanisms, trends, and perspectivesReview Articlehttps://doi.org/10.15480/882.1697710.1016/j.nxmate.2026.10197110.15480/882.16977