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The alternative methods of stabilizing soil utilizing agro-industrial waste have gained popularity due to the growing need for sustainable infrastructure solutions in tropical regions. This review explores the potential of four underutilized pozzolanic materials: Rice Husk Ash (RHA), Fly Ash (FA), Palm Oil Fuel Ash (POFA), and Corn Cob Ash (CCA), as eco-friendly binders for road subgrade and subbase stabilization. These materials are rich in amorphous silica and alumina, and present a promising alternative to traditional cement and lime stabilizers, which are both cost- and carbon-intensive. The article provides a comprehensive evaluation of the physicochemical properties of RHA, FA, POFA, and CCA, alongside their reaction mechanisms when mixed with problematic tropical soils. Drawing from a wide range of laboratory studies and case examples, the review compares their performance in improving strength characteristics (Unconfined Compressive Strength and California Bearing Ratio), compaction behavior, and durability under moisture and thermal cycles. While RHA and FA are relatively well-documented for their pozzolanic reactivity and consistent stabilization outcomes, POFA and CCA, though less researched demonstrate comparable potential when properly processed and blended. Beyond technical performance, the review highlights the environmental and economic benefits of using agro waste pozzolans, especially in regions where these wastes are abundant and often disposed of unsustainably. The use of these materials contributes to circular economy practices by reducing landfill waste, lowering greenhouse gas emissions, and offering cost-effective alternatives to conventional stabilizers. However, the review also identifies key research gaps, including variability in ash composition, lack of field-scale validation, and the absence of standardized guidelines for pozzolan-based soil stabilization. It calls for further investigation into synergistic blends (e.g., RHA + CCA or FA + POFA), long-term durability studies, and the development of predictive models to optimize mix design based on local material properties.