Integrating artificial intelligence (AI) in emergency medical (EM) has been proven to be transformative by improving specific key performance indicators (KPIs) such as patient wait times, treatment accuracy, and resource allocation to enhance performance efficiency and clinical outcomes. This paper examines the application of AI in EM with a focus on KPIs. AI features such as predictive analytics and machine learning increase patient flow, enhance staff productivity, and reduce waiting time. Moreover, AI utilization is limited by several factors, including data privacy concerns, Lack and scarcity of transparency in decision-making, and regulatory barriers. This paper addresses these barriers to the future use of AI in EM and healthcare, focusing on the importance of model interpretability, data standardization, and adaptive regulatory frameworks. The focus is on integrating AI to increase efficiency, improve outcomes, and optimize the use of resources in emergency healthcare.