Intraoperative trauma resuscitation necessitates rapid and precise decision-making across multiple critical domains, including differential diagnosis, hemodynamic management, volume resuscitation, blood product supplementation, coagulation adjustment, mechanical ventilation strategies, acid-base correction, and therapeutic interventions. The integration of artificial intelligence (AI) into this complex clinical landscape holds the promise of enhancing patient care and outcomes. This research project investigates the pivotal role of AI in optimizing intraoperative trauma resuscitation by harnessing its analytical capabilities to assist healthcare professionals. Our comprehensive methodology encompasses data collection from retrospective and prospective trauma cases, the development of AI machine learning algorithms, seamless integration into clinical workflows, and rigorous evaluation of the impact of AI-assisted decision-making. Through this research, we anticipate improvements in the accuracy and speed of differential diagnosis, facilitate hemodynamic stability, guide individualized volume resuscitation strategies, streamline blood product supplementation, optimize ventilation, assist with coagulation adjustments, enhance acid-base correction, resulting in more timely interventions. AI-guided intra-operative management promises to enhance patient stabilization and overall outcomes. The study aims to assess the impact of AI-assisted decision-making on patient survival, complication rates, and resource utilization. In conclusion, this research project aspires to demonstrate that AI can revolutionize the clinical management of intraoperative trauma resuscitation, offering the potential for improved patient outcomes and more efficient healthcare resource utilization. The findings herein will contribute valuable insights towards the integration of AI into trauma care, reaffirming its potential as a transformative tool in the realm of critical healthcare decision-making.