Recycled concrete aggregate (RCA) has an important role to play in reducing CO2 emissions and encouraging sustainable practices in the construction industry. This study introduces Optical Microscope Algorithm - Moment Balance Machine (OMA-MBM) as an innovative AI-based inference engine to improve the accuracy of RCA strength prediction. MBM improves the principles of Support Vector Machine (SVM) by considering moments to identify the optimal moment hyperplane. Backpropagation Neural Network (BPNN) is employed for weight assignment, and OMA is utilized for parameter optimization. The performance of the developed OMA-MBM was benchmarked against several machine learning models, including SVM, BPNN, PSO-SVM, and GWO-SVM, achieving the lowest error metrics with RMSE values of 9.711, 0.708, and 0.709 for compressive, flexural, and tensile strengths, respectively. It also scored the highest overall performance, represented by Reference Index of 1.000. Moreover, the model was successfully used to identify optimal RCA mixture designs in terms of achieving designated material strength requirements with the lowest CO2 emissions. The robustness of model predictions was further validated across diverse applications, i.e., Boston housing prices, vehicle fuel consumption, and building energy efficiency. These findings confirm the robustness and generalizability of OMA-MBM as an accurate predictive tool for development of sustainable construction.