This research presents an advanced AI-driven approach for food calorie estimation, leveraging state-of-the-art deep learning techniques. Building upon existing Convolutional Neural Network (CNN) methodologies, this study introduces innovations in data augmentation, multi-task learning, 3D image processing, transfer learning, and real-time analysis. These enhancements aim to improve the accuracy and robustness of dietary assessments in diverse environments. Preliminary results demonstrate significant strides in food recognition accuracy, portion size estimation, and calorie counting, holding promise for a more integrated and user-friendly approach to dietary management and health informatics.