Mobile applications powered by AI are transforming user experiences across industries. In this comprehensive guide, we'll build a Flutter application that integrates machine learning capabilities for real-time image analysis and prediction. Using the knee osteoarthritis classifier as a case study, we'll implement camera integration, image preprocessing on-device, secure API communication with backend ML services, and offline model deployment using TensorFlow Lite. The tutorial covers Flutter best practices for handling camera permissions, optimizing image quality for ML inference, implementing secure authentication, and providing meaningful user feedback for AI predictions. We'll also explore advanced topics like batch processing, result caching, and handling network connectivity issues. This approach has enabled me to deploy mobile AI applications that provide clinical-grade insights with 99.7% uptime and excellent user satisfaction ratings.