AI-driven Clinical Decision Support Systems (CDSS) are essential tools in modern healthcare, enhancing clinical decision-making and patient outcomes. This review explores the role of artificial intelligence (AI) in transforming CDSS, its applications in healthcare decision-making, associated challenges, and the potential trajectory toward fully realizing AI-CDSS's potential. CDSS are computer programs that assist healthcare professionals in making medical decisions, providing actionable insights, evidence-based recommendations, and patient-specific information at the point of care. AI technologies, including machine learning, natural language processing (NLP), and deep learning, have revolutionized CDSS, enabling it to process and interpret vast amounts of healthcare data with unprecedented speed and accuracy. Machine learning algorithms, such as neural networks and decision trees, allow CDSS to analyze patient data and make predictions, aiding in diagnosis and treatment planning. NLP enables CDSS to extract insights from clinical text, streamlining documentation and facilitating data retrieval. Deep learning models, such as convolutional neural networks (CNNs), excel at finding complex patterns and are used in image analysis, pattern recognition, and predictive modeling. These technologies enhance diagnostic accuracy, personalize treatment recommendations, and improve patient outcomes. However, challenges such as interpretability, bias, and ethical considerations must be addressed to ensure the effective and safe integration of AI into CDSS. User-centered design is crucial for ensuring that AI-CDSS is intuitive, efficient, and trusted by healthcare professionals. The review emphasizes the need for interdisciplinary collaboration, workflow alignment, and ethical considerations to realize the full potential of AI-CDSS in healthcare. By addressing these challenges and leveraging AI technologies, healthcare can achieve improved patient outcomes and more efficient clinical decision-making.AI-driven Clinical Decision Support Systems (CDSS) are essential tools in modern healthcare, enhancing clinical decision-making and patient outcomes. This review explores the role of artificial intelligence (AI) in transforming CDSS, its applications in healthcare decision-making, associated challenges, and the potential trajectory toward fully realizing AI-CDSS's potential. CDSS are computer programs that assist healthcare professionals in making medical decisions, providing actionable insights, evidence-based recommendations, and patient-specific information at the point of care. AI technologies, including machine learning, natural language processing (NLP), and deep learning, have revolutionized CDSS, enabling it to process and interpret vast amounts of healthcare data with unprecedented speed and accuracy. Machine learning algorithms, such as neural networks and decision trees, allow CDSS to analyze patient data and make predictions, aiding in diagnosis and treatment planning. NLP enables CDSS to extract insights from clinical text, streamlining documentation and facilitating data retrieval. Deep learning models, such as convolutional neural networks (CNNs), excel at finding complex patterns and are used in image analysis, pattern recognition, and predictive modeling. These technologies enhance diagnostic accuracy, personalize treatment recommendations, and improve patient outcomes. However, challenges such as interpretability, bias, and ethical considerations must be addressed to ensure the effective and safe integration of AI into CDSS. User-centered design is crucial for ensuring that AI-CDSS is intuitive, efficient, and trusted by healthcare professionals. The review emphasizes the need for interdisciplinary collaboration, workflow alignment, and ethical considerations to realize the full potential of AI-CDSS in healthcare. By addressing these challenges and leveraging AI technologies, healthcare can achieve improved patient outcomes and more efficient clinical decision-making.