The significance of nutrition is increasingly being recognized and personal dietary advice is required, therefore making it essential to develop intelligent diet planning systems. Wellness Wizard, a diet planner developed with Flask, which applies machine learning algorithms for personalized meal recommendations based on unique user characteristics, is described in this paper. The system receives age, food preference, weight, height, health goal and gender from the users as inputs before generating specific dietary needs that can be used to design a meal plan. It relies on food database and pre-calculated nutritional distribution data for food selection purposes. K-Means clustering categorizes foods while Random Forest Classifier predicts optimal meals in this application. The aim of the diet planner is to recommend balanced diets that are customized for losing weight or gaining weight or maintaining good physique. The application is accessed through an uncomplicated online form and dynamic mark-up HTML template for generating outcomes. Combining data science with web technologies demonstrates a powerful way of addressing personalized dietary recommendations, achieving an accuracy of over 87%.