This study introduces a novel approach to heart stroke prediction, leveraging the synergy of Internet of Things (IoT) and advanced cloud-based machine learning algorithms. Utilizing a comprehensive dataset from 600 individuals, the research focuses on real-time monitoring of critical vitals such as body temperature, pulse rate, blood pressure, and oxygen saturation. Innovative machine learning models including Random Forest, Artificial Neural Networks, and Gradient Boosting are employed to analyze the data, offering a predictive mechanism with enhanced accuracy and reliability. The paper underscores the transformative potential of continuous monitoring in predictive healthcare, aiming to revolutionize early heart stroke detection and prevention strategies. This approach exemplifies a significant stride in integrating technology with healthcare, paving the way for future advancements in proactive medical interventions. In this research, we present a groundbreaking method for predicting heart strokes by integrating IoT-based continuous vital sign monitoring with sophisticated cloud-based machine learning. Our approach, tested on a dataset from 600 individuals, utilizes real-time data on body temperature, pulse, blood pressure, and oxygen saturation. We applied advanced machine learning techniques, such as Random Forest and Gradient Boosting, achieving significant strides in prediction accuracy. This study not only enhances heart stroke prediction but also sets a new benchmark in the integration of technology and healthcare, opening avenues for proactive medical interventions. In this research, we introduce an innovative heart stroke prediction method that synergizes IoT-based continuous monitoring and advanced cloud-based machine learning, utilizing a custom-developed device with state-of-the-art sensors. Data from 600 individuals, featuring real-time body temperature, pulse, blood pressure, and oxygen saturation, were analyzed using sophisticated machine learning models like Random Forest and Gradient Boosting. This approach not only advances heart stroke prediction accuracy but also marks a significant leap in integrating cutting-edge technology with healthcare, heralding a new era of proactive medical interventions. This research presents a novel heart stroke prediction model using a custom-built IoT device with advanced sensors. The data, collected from 600 individuals, was analyzed using various machine learning techniques. Linear Regression showed the highest performance with an AUC of 0.992, CA of 0.96, and F1 score of 0.96, followed closely by Gradient Boosting with similarly high metrics. Random Forest, Neural Networks, and Tree models also demonstrated substantial efficacy. These results highlight the potential of integrating modern sensor technology and diverse analytical methods in predictive healthcare, setting a new standard in early heart stroke detection.