The human musculoskeletal (MSK) system (also known as the locomotor system) provides strength and assistance to perform functional tasks and daily life activities. The MSK health monitoring plays a vital role in maintaining the body mobility and quality of life. Manual approaches for musculoskeletal health monitoring are subjective and require a clinician's intervention. The evolution in motion tracking technology enables us to capture the fine details of body movements. The research community has proposed various approaches to help clinicians in diagnosis and monitor treatment sessions. This paper succinctly reviews the evolution of technology-assisted approaches for musculoskeletal health monitoring, using motion capture sensors. To streamline the search through the literature database, the PICOS framework and PRISMA method have been incorporated. The present study reviews methods to transform motion capture data into kinematics variables and factors that affect the tracking performance of RGB-D sensors. Furthermore, widely utilized time-series filters for skeletal data denoising and smoothing for kinematics analysis, stochastic models for movement modeling, rule-based and template-based approaches for rehabilitation exercises assessment, and telerehabilitation sessions for remote health monitoring are explored. This article analyzes skeletal tracking methods by providing advantages and drawbacks of the state of the art rehabilitation sessions assessment, skeletal joint kinematics analysis, and MSK Telerehabilitation approaches. It also discusses the possible future research avenues to improve musculoskeletal disorder diagnosis and treatment monitoring. Our review signifies that RGB-D sensor-based approaches are inexpensive and portable for disorder diagnosis and treatment monitoring. It can also be a viable option for clinicians to provide contactless healthcare access to patients in the current scenario of the COVID-19 pandemic. © 2021 Elsevier Ltd