Artificial intelligence (AI) and machine learning (ML) are increasingly applied in lumbar spine surgery to improve decision-making, surgical accuracy, and patient care. This chapter describes key AI and ML techniques, including supervised learning for outcome prediction, unsupervised learning for identifying patient subgroups, reinforcement learning for optimizing robotic procedures, and natural language processing for analyzing clinical notes. It also examines their use throughout the surgical process, including preoperative planning, intraoperative guidance, and postoperative monitoring. Preoperatively, AI tools assist in forecasting outcomes and estimating risks using predictive models derived from patient-specific data and radiographic imaging. These tools can help guide the surgeon tailor surgical plans to individual patients. Intraoperative systems integrate AI-driven navigation and robotics, allowing for real-time feedback and increased hardware placement precision, reducing variability and minimizing surgical errors. Postoperatively, AI models predict complications and allow for timely interventions, while remote monitoring tools analyze data from wearable devices to track patient recovery and update follow-up plans as needed. Despite their potential, challenges such as ensuring high-quality data, achieving model generalizability across diverse populations, and addressing ethical issues like patient privacy and algorithmic bias remain critical considerations. Future directions focus on further integration of AI with robotic systems, augmented reality (AR), and virtual reality (VR) to optimize surgical planning and intraoperative guidance. As AI continues to evolve, these technologies are expected to improve surgical precision, reduce complications, and reduce variations in surgical practices across patient populations.