Abstract: The article discusses a new methodology for improving AI-based decision support systems (DSS) using advanced algorithms and data processing techniques. Key Points: The research introduces a hybrid model that integrates machine learning and expert systems to enhance decision-making accuracy. A comprehensive analysis of existing DSS frameworks reveals limitations in data handling and adaptability, which the proposed model addresses by employing adaptive learning mechanisms and real-time data analytics. The model's effectiveness is demonstrated through various simulations and real-world applications, showing significant improvements in efficiency and outcome reliability. Conclusion: The study concludes that the integration of adaptive AI technologies in DSS can lead to more robust and flexible decision-making tools, crucial for sectors like healthcare, finance, and logistics, where real-time decision accuracy is paramount. The authors suggest further research into scalability and integration with existing IT infrastructures to fully leverage this technology's potential.