Concrete is the second most consumed material after water, with cement as its primary binder. However, cement production accounts for nearly 7% of global CO₂ emissions, posing a major sustainability challenge. This review critically evaluates 35 agricultural biomass ashes (ABAs) as potential supplementary cementitious materials (SCMs) for partial cement replacement, focusing on their effects on concrete strength and durability and highlighting performance gaps. Using a systematic methodology, rice husk ash (RHA), sugarcane bagasse ash (SCBA), and wheat straw ash (WSA) were identified as the most promising ABAs, enhancing strength and durability—including resistance to chloride ingress, sulfate attack, acid exposure, alkali–silica reaction, and drying shrinkage—while maintaining acceptable workability. Optimal replacement levels are recommended at 30% for RHA and 20% for SCBA and WSA, balancing performance and sustainability. These findings indicate that ABAs are viable, scalable SCMs for low-carbon concrete, promoting greener construction and contributing to global climate mitigation.

Agricultural biomass ashes are increasingly used as sustainable supplementary cementitious materials (SCMs) to reduce cement-related carbon emissions and improve concrete performance. However, their effects on compressive strength depend on the SCM type, replacement level, and physical and chemical properties. These variables are often overlooked in machine learning studies focused on single SCM types and absolute strength prediction, limiting transferability across heterogeneous SCM datasets. This study develops an interpretable machine learning framework using a compiled dataset covering 18 agricultural biomass ash SCMs (bio-SCMs) used in concrete. Input features include concrete mixture proportions, the SCM replacement level, chemical composition, and specific surface area (SSA), while the target variable is the 28-day compressive-strength ratio relative to the companion control mixture. Among the five evaluated models, XGBoost achieved the best performance, with weighted 10-fold cross-validation R² values around 0.80. SHapley Additive exPlanations (SHAP) results were interpreted as model associations rather than causal mechanisms. Higher SCM SiO₂ content, pozzolanic oxide content, superplasticizer dosage, and baseline control mixture strength were associated with more favorable strength ratios; SCM SSA showed a mild positive tendency, whereas a higher SCM replacement level, water-to-binder ratio, and loss on ignition were associated with less favorable strength ratios. SCM-specific response analysis further identified literature-derived screening ranges based on observed and interpolated replacement levels rather than machine learning extrapolation.