AI-Driven Predictive Analytics for Smart and Sustainable Engineering Applications

Abstract

The proposal on AI-based predictive analytics model on smart and sustainable engineering application is proposed in this research paper. The model integrates Artificial Neural Networks (ANN), Random Forest (RF) and Support Vector machines (SVM) to create an ensemble architecture to enhance the predictive accuracy, robustness and sustainability. The framework was evaluated along different dimensions that included smart manufacturing, energy load prediction, sustainable infrastructure planning, and predictive maintenance systems. Results differ to show that the hybrid model is more precise, along with RMSE and R 2 score, and the outputs of this model give a measurable improvement in terms of energy use, emission, and reliability. It is the sensitivity analysis and cross-validation that make sure that the model is stable under various working conditions. The findings indicate a positive relationship between predictive accuracy and sustainability performance, which indicate the feasibility and environmental benefits of AI-based decision-making. Overall, the proposed framework has the potential to present a flexible and efficient method of introducing intelligent analytics into modern engineering to achieve the long-term sustainability objectives.