Disease prediction depending upon symptoms of patient at any age is very important in health industry. The advancement in computer vision algorithms leads to prediction of diseases based upon one or multiple symptoms. The advanced disease prediction system employs a diverse array of machine learning algorithms, including extra trees, Gaussian naive Bayes, bagging, stochastic gradient descent, linear discriminant analysis, nearest centroid, passive aggressive classifier, ridge classifier, and various optimized models using GridSearch and Halving GridSearch. This comprehensive approach aims to accurately predict diseases based on presented symptoms. The system employs advanced data preprocessing methods, extensive model training using various algorithms, comprehensive evaluation, and accurate disease prediction. Most accurate models are Gaussian Naive Bayes (92.76%), GridSearch decision tree (92.76%), and GridSearch K-nearest neighbor (92.70%).