The global construction industry, driven by concrete consumption, faces a dual challenge of environmental sustainability and resource depletion. Traditional prescriptive standards for concrete mix design often inhibit innovation and the efficient utilization of industrial and agricultural waste materials. This review paper proposes a paradigm shift towards a unified performance-based specification (PBS) framework for "green concrete." The framework moves beyond prescriptive recipes, specifying concrete by its performance metrics, including mechanical strength, durability indices, and environmental impact (e.g., CO₂ footprint). We synthesize data from numerous studies on partial cement replacement with materials like Marble Dust Powder (MDP), Rice Husk Ash (RHA), Sugarcane Bagasse Ash (SCBA), and Waste Paper Sludge Ash (WPSA) to establish performance benchmarks. Crucially, the framework integrates advanced monitoring technologies, such as remote sensing, computer vision, and machine learning (ML) models, for real-time, non-destructive compliance verification. We explore the application of ML techniques including Frequency Ratio (FR), Logistic Regression (LR), Artificial Neural Networks (ANN), and Weight of Evidence (WoE) adapted from geotechnical and environmental engineering to predict long-term durability and automate quality control. This integrated approach promises to accelerate the adoption of sustainable concrete, ensuring structural integrity while minimizing ecological impact through a data-driven, transparent, and agile system.