Artificial intelligence (AI) is increasingly being integrated into surgery, offering new tools for diagnosis, treatment, and patient management. This review explores the current applications of AI in various surgical disciplines, including general surgery, cardiothoracic surgery, vascular surgery, urology, neurosurgery, and orthopedic surgery. AI, particularly through machine learning (ML), enables machines to learn from data, recognize patterns, and make predictions, enhancing diagnostic accuracy and treatment outcomes. AI has shown promise in improving surgical site infection prediction, postoperative complication risk assessment, and intraoperative decision-making. In cardiothoracic surgery, AI has been used to predict postoperative outcomes and improve diagnostic accuracy. In vascular surgery, AI aids in image segmentation, risk assessment, and surgical planning. In urology, AI assists in diagnosing conditions like prostate cancer and urinary tract infections. In neurosurgery, AI contributes to the detection of brain tumors and intracranial hemorrhage. In orthopedic surgery, AI helps in fracture detection, cartilage analysis, and surgical planning. Despite its potential, AI faces challenges such as data bias, ethical concerns, and the need for robust validation. The integration of AI into surgery is still in its early stages, but it holds significant promise for improving patient care and surgical outcomes. Future developments in AI, combined with advancements in robotics and computer vision, may further enhance the role of AI in surgery, leading to more precise and efficient surgical procedures. However, ensuring the safety, reliability, and ethical use of AI in surgery remains a critical challenge.Artificial intelligence (AI) is increasingly being integrated into surgery, offering new tools for diagnosis, treatment, and patient management. This review explores the current applications of AI in various surgical disciplines, including general surgery, cardiothoracic surgery, vascular surgery, urology, neurosurgery, and orthopedic surgery. AI, particularly through machine learning (ML), enables machines to learn from data, recognize patterns, and make predictions, enhancing diagnostic accuracy and treatment outcomes. AI has shown promise in improving surgical site infection prediction, postoperative complication risk assessment, and intraoperative decision-making. In cardiothoracic surgery, AI has been used to predict postoperative outcomes and improve diagnostic accuracy. In vascular surgery, AI aids in image segmentation, risk assessment, and surgical planning. In urology, AI assists in diagnosing conditions like prostate cancer and urinary tract infections. In neurosurgery, AI contributes to the detection of brain tumors and intracranial hemorrhage. In orthopedic surgery, AI helps in fracture detection, cartilage analysis, and surgical planning. Despite its potential, AI faces challenges such as data bias, ethical concerns, and the need for robust validation. The integration of AI into surgery is still in its early stages, but it holds significant promise for improving patient care and surgical outcomes. Future developments in AI, combined with advancements in robotics and computer vision, may further enhance the role of AI in surgery, leading to more precise and efficient surgical procedures. However, ensuring the safety, reliability, and ethical use of AI in surgery remains a critical challenge.