The Internet of Medical Things (IoMT) and Machine Learning (ML) are two critical technologies that have shown significant potential in improving patient care, diagnosis, and treatment. This study aims to investigate the design, implementation and significance of an IoMT-enabled platform capable of exploiting multiple ML models for healthcare analytics, focusing on cervical cells, colorectal polyps, retinas and skin lesions. New opportunities and challenges could be identified when IoMT and ML principles are combined with Digital Twins (DT). DT can enable real-time simulation, monitoring, and optimization of IoMT-enabled healthcare platforms by creating virtual replicas of patient data and ML model workflows. This could enhance predictive analytics and personalized care.