Background: Emergency medicine is a critical field where timely and accurate diagnosis and treatment decisions can be a matter of life and death. With the quick progressions in artificial intelligence (AI) and machine learning (ML) technologies, here is an increasing interest in harnessing these tools to enhance the early diagnosis and treatment decisions in emergency medicine. This study discovers possible applications, challenges, and inferences of AI and ML in this context. Aim: The primary aim of this research is to investigate how AI and ML can improve early diagnosis and treatment decisions in emergency medicine. We aim to assess present state of AI and ML integration in emergency departments, identify their strengths and limitations, and provide insights into the future possibilities for optimizing patient care. Methods: This study employs a comprehensive review of literature, including peer-reviewed articles, case studies, and reports, to examine part of AI and ML in emergency medicine. We analyze the various AI and ML algorithms, data sources, and models used for early diagnosis and treatment recommendations. Additionally, we assess the ethical, legal, and practical thoughts nearby implementation of AI and ML in emergency settings. Results: Our findings reveal that AI and ML have shown promising results in improving the accuracy and speed of diagnosis in emergency medicine. These technologies can assist in image analysis, risk prediction, and resource allocation. However, challenges such as data quality, algorithm interpretability, and integration into existing healthcare systems required to remain addressed to understand their full possibility. Conclusion: The integration of AI and ML in early analysis and treatment decisions in emergency medicine holds significant promise. These technologies can augment the capabilities of healthcare professionals, leading to faster and more accurate diagnoses. However, a thoughtful approach is required to address ethical and practical concerns. Future research should focus on refining AI and ML algorithms, enhancing data security, and establishing clear guidelines for their deployment in emergency settings.