The global construction industry faces a dual challenge: meeting the massive demand for concrete while mitigating its significant environmental footprint, primarily from cement production. Concurrently, the disposal of industrial and agricultural waste poses severe ecological threats. The integration of these waste streams such as Sugarcane Bagasse Ash (SCBA), Waste Paper Sludge Ash (WPSA), Rice Husk Ash (RHA), Fly Ash, and Waste Glass Powder (WGP) as partial cement replacements presents a promising pathway toward sustainable concrete. However, the non-linear and complex behavior of concrete incorporating these supplementary cementitious materials (SCMs) makes traditional empirical mix design methods inadequate. This paper provides a comprehensive review of the state-of-the-art in leveraging advanced Artificial Intelligence (AI) and Machine Learning (ML) models to optimize sustainable concrete mix designs. We synthesize empirical findings from numerous studies on the mechanical and durability properties of concrete containing SCBA, WPSA, RHA, Fly Ash, and WGP. Building upon this foundation, the core of this review proposes a novel multi-method AI framework. This integrated framework synergistically combines Geographic Information Systems (GIS) for spatial waste inventory and logistics, Remote Sensing for monitoring raw material availability and environmental impact, and a suite of advanced ML algorithms including Frequency Ratio (FR), Information Value (IV), Logistic Regression (LR), Artificial Neural Networks (ANN), and Weight of Evidence (WoE) to create a robust predictive and optimization model. The proposed system is designed to predict key concrete properties (e.g., compressive and tensile strength) and identify the optimal mix proportion for a given set of performance, cost, and sustainability criteria. This review underscores the transformative potential of a data-driven, AI-powered approach in transitioning the concrete industry towards a circular economy, enabling the effective Valorization of waste streams into high-value construction materials.