Healthcare faces an unprecedented inflection point driven by rapidly aging populations, exponential increases in procedural demand, and persistent clinical challenges including implant failure, infections, and outcome variability. This chapter explores how artificial intelligence (AI) and digital twin technologies are emerging as transformative solutions to address these pressures and fundamentally reshape musculoskeletal care. We begin by examining the foundational concepts of AI relevant to orthopedic practice, including machine learning, deep learning, and foundation models that transition from narrow, task-specific algorithms to versatile, general-purpose platforms. Particular emphasis is placed on explainable AI (XAI) techniques that render algorithmic decision-making transparent and clinically interpretable, addressing the critical “black box” barrier to surgical adoption. The clinical applications of AI span the entire care continuum. In diagnostics and preoperative planning, deep learning models demonstrate remarkable proficiency in image analysis, fracture detection, and pathology classification, while predictive analytics enable hyper-personalized surgical planning and individualized risk stratification for complications. Postoperative care is revolutionized through smart implants embedded with biosensors and wearable technology, generating continuous data streams that AI algorithms process to create dynamic, adaptive rehabilitation protocols and enable early complication detection. Digital twins represent the next evolutionary leap—dynamic, patient-specific virtual replicas that integrate multimodal data streams including medical imaging, biomechanical analysis, genomic profiles, and real-time sensor data. We delineate the progression from static anatomical models to functional twins incorporating biomechanical simulation and ultimately to intelligent twins with closed-loop adaptive capabilities. Clinical applications include virtual surgical rehearsal with biomechanical consequence modeling, long-term outcome prediction through finite element analysis, and augmented reality-guided surgery that overlays digital twin insights directly into the operative field. The chapter concludes by addressing critical challenges including algorithmic transparency, data privacy, regulatory validation frameworks, and the surgeon’s evolving role in this synergistic human-machine partnership that augments rather than replaces clinical expertise.