The precast concrete industry stands at a pivotal juncture, driven by the dual imperatives of enhancing sustainability and embracing digitalization. The utilization of industrial and agricultural waste by-products, such as marble dust, rice husk ash, sugarcane bagasse ash, and waste paper sludge ash, in concrete mixes presents a significant opportunity for reducing the environmental footprint of construction. However, the inherent variability in the chemical and physical properties of these supplementary cementitious materials (SCMs) introduces significant challenges in ensuring consistent workability, strength, and durability of the final precast elements. This review paper posits that the synergistic integration of robotic automation and artificial intelligence (AI) is the key to unlocking the full potential of these sustainable, yet non-standard, concrete mixes. We explore the state-of-the-art in robotic systems for precise handling, casting, and finishing of precast elements, which can mitigate the variability introduced by novel mix designs. Furthermore, we delve into the critical role of AI and machine learning (ML) for real-time quality control, predicting final mechanical properties, and optimizing mix proportions. The paper proposes a novel framework that leverages advanced ML techniques including Computer Vision for defect detection, and predictive models like Artificial Neural Networks (ANNs) and Logistic Regression to create a closed-loop, intelligent manufacturing system. By reviewing and synthesizing contemporary research, this paper outlines a pathway towards a resilient, data-driven, and environmentally responsible precast concrete industry.