Chronic diseases present significant healthcare challenges worldwide, affecting mortality rates and straining healthcare budgets. Recent advancements in medical research have enabled the collection of health-related data, integrating structured and unstructured information to enhance accuracy. Machine learning techniques and intelligent healthcare solutions have emerged as valuable tools in mitigating risks, detecting diseases at an early stage, and effectively managing chronic conditions. To improve prediction accuracy, preprocessing methods have been developed to address missing data and select relevant features. Machine learning models leverage user-input symptoms and online activities to predict prevalent chronic diseases like diabetes, cardiovascular diseases, and cancer. Mobile applications empower patients to take charge of their health by facilitating self-management of their conditions. In this context, the proposed system utilizes K-Nearest Neighbors (KNN) and Convolutional Neural Networks (CNN) for the detection of chronic diseases. Overall, the integration of machine learning techniques and healthcare data shows great potential for disease forecasting and management. By harnessing the power of data analysis and predictive modeling, healthcare professionals can make more informed decisions, improve patient outcomes, and optimize resource allocation