In the time-critical, unpredictable, and complex environments of prehospital emergency care, timely and accurate decision-making greatly affects patients’ outcomes. In that high-stress environment, emergency medical service providers’ decision-making is limited, leading to delays and diagnostic errors. To address this predicament, clinical decision support systems have been introduced to provide cognitive and medical support, but their traditional rule-based approaches remain insufficient. This paper explores the potential of intelligent prehospital clinical decision support systems (PH-CDSS), powered by artificial intelligence, real-time, data-driven systems, and integration platforms to overcome the limitations of current PH-CDSS. For that, current PH-CDSS are analyzed to identify challenges, and then these challenges are mapped to emerging technological solutions. Plus, illustrative applications such as AI-driven stroke triage, predictive analytics for patient health degradation, and telemedicine-enabled decision support are highlighted. The findings suggest that intelligent PH-CDSS can enhance resource allocation, improve diagnostic accuracy, reduce adverse events, and streamline communication across the care continuum, ultimately improving patients’ outcomes. Despite this potential, Intelligent PH-CDSS faces many barriers, for which future research direction are provided.