<![CDATA[Decision-making in highly complex systems is increasingly challenged by uncertainty, dynamic environments, and the availability of large-scale, high-dimensional data. Traditional optimization methods often lack adaptability, while standalone Artificial Intelligence models struggle to explicitly handle uncertainty in a principled manner. To address these limitations, this research proposes a unified framework that integrates Artificial Intelligence with Stochastic Optimization for enhanced decision-making in complex and uncertain environments. The proposed framework combines data-driven learning and probabilistic optimization within a closed-loop architecture consisting of data input, AI-based prediction, stochastic decision-making, and continuous feedback. Advanced AI models, including deep learning and reinforcement learning, are employed to extract patterns and generate predictive insights from real-time and historical data. These outputs are then incorporated into stochastic optimization models, which evaluate decisions under uncertainty using probabilistic constraints and scenario-based analysis. The framework is further strengthened by an adaptive feedback mechanism that continuously updates both learning and optimization components. Experimental evaluation demonstrates that the proposed approach outperforms traditional optimization and pure AI models in terms of decision accuracy, robustness under uncertainty, and adaptability to dynamic environments. The framework also shows improved stability and computational efficiency when applied to large-scale systems. Practical applications in domains such as finance, logistics, and smart city management highlight its real-world relevance. Overall, this research contributes to decision science by bridging the gap between learning and uncertainty modeling, providing a scalable and integrated solution for intelligent decision-making in highly complex systems.]]>
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