Fusion Of Computational Mathematics And Artificial Intelligence For Intelligent Decision-Making In Engineering And Technology

The growing complexity of the contemporary engineering systems requires intelligent decision-making structures that can deal with nonlinear dynamics, high-dimensional data, and operational uncertainty. Current paper suggests a hybrid computing system, which combines computational mathematics and artificial intelligence to facilitate optimization, prediction and stability of the system in engineering processes. The model integrates mathematical modelling and constraint-based optimisation with machine learning methods in order to provide both theoretical rigour and adaptive learning. To confirm the performance improvements, descriptive and inferential statistical analysis was performed showing statistically significant improvements when compared to standalone optimization and machine learning-based models. Improved predictive reliability and the stability of convergence are verified by the performance evaluation metrics, such as accuracy, RMSE, precision, recall, and F1-score. The practicality and scalability of the proposed framework are also emphasized in the case studies of smart manufacturing optimization, energy load forecasting and predictive maintenance. The hybrid nature(mathematically constrained) guarantees feasibility besides utilizing AI in nonlinear pattern identification and nonlinear adaptability in real-time. In general, the integration offers a powerful, scalable, and computationally efficient solution with intelligent engineering decision systems in complex and data-intensive application environments.