This study examines the integration of artificial intelligence (AI)-supported decision support systems (DSS) with management information systems (MIS), addressing their transformative impact on business decision-making processes. In today’s business landscape, the increasing volume and complexity of data render traditional decision-making methods inadequate, highlighting the importance of AI-supported DSS, which offer data-driven, dynamic, and automated solutions. Technologies such as machine learning and deep learning enhance DSS capabilities in areas like big data analytics, forecasting, and optimization, thereby improving operational efficiency and providing a strategic competitive advantage for businesses. AI-supported DSS can analyze both structured and unstructured data, learning from historical data to generate future-oriented scenarios. However, their success hinges on factors such as data quality, model transparency, scalability, and security. The “black box” nature of deep learning models poses challenges to transparency and interpretability, which explainable AI (XAI) approaches aim to address. Additionally, data security and ethical considerations significantly influence system applicability, with methods like anonymization, encryption, and access control gaining prominence. The integration of AI-DSS with MIS supports strategic planning in domains such as finance, supply chain management, and human resources, yet it requires balancing technical, managerial, and ethical dimensions. Looking ahead, innovations like quantum computing and continuous learning are expected to enhance these systems’ potential. Businesses should invest in these technologies to strengthen their data-driven decision-making capabilities while adhering to ethical and security standards.