Application of IoT and machine learning techniques in cancer diagnosis has gained significant attention in recent years. This research article presents a comprehensive study on empowering cancer diagnosis through the integration of IoT and machine learning approaches. The primary focus is on monitoring the health of patients using a variety of sensors, including temperature, pressure, heart rate, and ECG. These sensors continuously collect data, which is transmitted to a microcontroller and subsequently to a local computer via a Raspberry Pi. The data is securely stored in cloud storage, allowing doctors to remotely access and monitor the health status of their patients. The research investigates the effectiveness of machine learning algorithms in predicting cancer based on protein datasets. Specifically, K-nearest neighbors (KNN), Naive Bayes, Decision Tree, and Support Vector Machine (SVM) algorithms are employed. These algorithms are trained using online libraries such as Pandas, Scikit-learn, and Keras. Performance evaluation metrics, including accuracy, precision, recall, specificity, and F1-score, are utilized to assess the efficacy of the algorithms. The findings highlight the superiority of the Decision Tree algorithm in terms of accuracy, precision, recall, specificity, and F1-score. This suggests that Decision Tree is a robust approach for accurate cancer prediction based on protein datasets. The research contributes to the advancement of cancer diagnosis methodologies by combining IoT technology, machine learning algorithms, and protein data analysis