Background: Artificial intelligence (AI) and machine learning (ML) have emerged as transformative technologies in healthcare, reshaping clinical decision-making, professional responsibilities, and patient clinician interactions. Their ability to analyze large, complex datasets has enabled more objective assessments, personalized treatment planning, and predictive analytics. In physiotherapy and rehabilitation, the integration of AI represents a significant advancement, particularly in clinical evaluation, functional assessment, and outcome prediction.

Aim: The aim of this review is to examine the role of artificial intelligence and machine learning in physiotherapy, with particular emphasis on their applications in clinical evaluation, rehabilitation assessment, and management, as well as their implications for decision support and patient care.

Methods and Results: A narrative review of the literature was conducted, focusing on studies that explored AI- and ML-based applications in physiotherapy assessment and rehabilitation management. Evidence indicates that machine learning models can predict rehabilitation outcomes, analyze movement quality, support pain assessment, and enable personalized exercise prescription. AI-driven systems, including activity recognition, fall detection, virtual reality–based rehabilitation, and wearable sensor technologies, have demonstrated potential to improve functional outcomes, patient engagement, and quality of life. Collaborative human–AI models further enhance clinical interpretability and support therapist involvement in decision-making.

Conclusion: Artificial intelligence and machine learning hold substantial promise for advancing physiotherapy practice by improving clinical evaluation, rehabilitation management, and decision support. However, successful integration requires harmonization of technological innovation with clinical expertise, ethical considerations, and patient-centered care. Continued research, clinician education, and evidence-based implementation are essential to ensure that AI-enhanced physiotherapy delivers equitable, efficient, and ethically sound healthcare outcomes.

Keywords: Artificial Intelligence; Machine Learning; Physiotherapy Assessment; Clinical Decision Support Systems.