In recent years, Data Mining has emerged as a crucial process for extracting valuable information from vast datasets across various industries, including the medical field. This study focuses on the application of machine learning techniques, specifically data classification, to develop an effective treatment plan for Spontaneous Homeopathic Syndrome. The evaluation process involves assessing the performance of one or multiple technically high-quality models. Rigorous scrutiny is conducted at every step of model construction to ensure the successful attainment of business objectives. Furthermore, the use of IoT-based classification enhances the predictive diagnosis and management of the syndrome. Through the integration of Data Mining, machine learning, and IoT technologies, this research aims to revolutionize the way Spontaneous Homeopathic Syndrome is diagnosed and managed, leading to improved patient outcomes and enhanced healthcare practices.