Detecting chronic diseases at an early stage is a major contributing factor to reducing healthcare expenses and increasing survival rates. Most diagnostic methods utilize laboratory tests and the physician’s expertise; therefore, it could take time for patients to receive timely treatment.This research presents a Multi-Disease Prediction and Appointment Management System that employs machine learning techniques in order to create an integrated solution for providing healthcare. The proposed system will predict four major diseases, including: diabetes, heart disease, Parkinson’s disease, and skin diseases. Specifically, Support Vector Machine (SVM) will be used to predict diabetes and Parkinson’s Disease, Logistic Regression will be used to predict heart disease, and Convolutional Neural Networks (CNN) will be used to categorize dermatological images. Medical datasets and images of skin conditions were collected from Kaggle; the datasets were then trained and tested in Google Colab. After training and testing the model in Google Colab, it was deployed via a web interface created with Streamlit. Additionally, Firebase will be utilized as a mechanism for both secure doctor/patient authentication and real-time appointment management. This study contributes to sustainable healthcare development by enhancing public health, improving health system access, and supporting inclusive health through machine learning–based disease prediction.