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The Increasing Demand For Health Awareness And Personalized Dietary Management Has Driven The Need For Automated Food Recognition And Nutritional Analysis Systems. Traditional Methods Of Food Logging, Such As Manual Entry Or Barcode Scanning, Are Often Inconvenient, Time-consuming, And Prone To User Error. Recent Advancements In Deep Learning, Particularly Convolutional Neural Networks (CNNs), Have Demonstrated Remarkable Success In Image Classification Tasks, Providing A Foundation For Intelligent Food Recognition. This Study Proposes An Automatic Food Image Classification System That Leverages Deep Learning Techniques To Accurately Identify Food Categories From Images And Estimate Their Corresponding Nutritional Values, Such As Calories, Protein, Carbohydrates, And Fat Content. The Framework Involves Preprocessing Of Food Images, Feature Extraction Through CNN-based Models, And Classification Into Predefined Food Categories. A Nutritional Database Is Integrated To Map Recognized Food Items To Their Nutritional Information, Enabling Precise Prediction Of Dietary Intake. Experimental Evaluations On Benchmark Food Image Datasets Demonstrate The Effectiveness Of The Proposed Approach In Achieving High Classification Accuracy And Reliable Nutrient Estimation. This System Has Significant Applications In Healthcare, Fitness Tracking, And Personalized Diet Management, Ultimately Contributing To Improved Lifestyle And Well-being.