Predictive Computational Models for AI-Enhanced Decision Support Systems

The artificial intelligence (AI) has massively changed the strategies of making decisions in key areas of life and business, including the field of healthcare, finance, disaster management, transport, and automation in industries. The contemporary Decision Support Systems (DSS) are turning more towards predictive models of computation to process vast amounts of data, create dynamic bodies of knowledge and improve decision making precision. The paper gives a comprehensive review and implementation plan of predictive computational models of AI-Enhanced Decision Support Systems (AI-DSS). The areas of the research include statistical prediction models, deep learning architecture, and reinforcement learning-based optimization as well as hybrid intelligent systems, which are aimed at strategic and operational decision-making. The paper identifies such issues as scalability, the generalization of models, uncertainty quantification, real-time inferences, data heterogeneity, and ethical governance as the key challenges. It introduces a new Adaptive Predictive Intelligence Framework (APIF) with combination of time-series forecasting, data fusion processes, knowledge graph, explainable AI (XAI), and multi-criteria optimization. Accuracy, F-score, Mean Absolute Percentage Error (MAPE) and computation complexity measurements are used to make performance evaluations using benchmark datasets. The suggested methodology possesses enhanced precision and latency of decisions, which justify dynamic decision situation. In this paper, there is an attempt to provide systematic methodology that aligns the computational intelligence with the domain- wise decision demands to give viable actionable subject to future research trends and industrial implementation. It focuses on clear, audit-able and reliable intelligent systems that can work autonomously with human decision-makers. Finally, AI-Enhanced DSS with the help of predictive computation models may build sustainable, data-driven governance and business transformation in the 21 st century