Decision-making is a fundamental activity in every domain, including healthcare, business management, finance, education, and governance. Traditional decision support systems are mostly rule-based and depend heavily on human expertise, which limits their accuracy, scalability, and adaptability. With the rapid growth of digital data, manual decision-making has become inefficient and error-prone. This paper presents an Intelligent Artificial Intelligence–Based System for Automated Decision Support that leverages machine learning techniques to analyze large datasets, identify hidden patterns, and generate accurate recommendations automatically. The proposed system integrates data preprocessing, learning models, a decision engine, and user interfaces in a scalable architecture. Experimental analysis shows that the AI-based decision support system improves decision accuracy, reduces processing time, and minimizes human intervention when compared to traditional approaches. Experimental results demonstrate that the proposed AI-based decision support system achieves higher decision accuracy, reduced processing time, and improved scalability when compared to traditional rule-based approaches.