This narrative review explores the transformative role of multimodal AI in orthopaedic surgery, emphasizing its ability to integrate diverse data sources such as radiological imaging, electronic health records, genomic information, and wearable device outputs. The study highlights AI's application across various stages of orthopaedic care, from presurgical prediction to intraoperative navigation and postoperative monitoring. Preoperative models demonstrate enhanced predictive performance with AUCs ranging from 0.79 to 0.98, improving tasks like tumor identification and osteoarthritis progression monitoring. Intraoperative AI systems enhance surgical precision, while postoperative wearable technologies provide dynamic assessments of rehabilitation progress. Despite its potential, the adoption of multimodal AI faces challenges like data integration, privacy concerns, and regulatory hurdles. Emerging technologies such as federated learning and edge computing are being explored to address these issues.