Brain tumors are deemed accurately diagnosed by MRI imaging for prompt treatment. This study presents a federated learning-enhanced edge network brain tumor prediction architecture that addresses data privacy, latency, and computational efficiency issues. The framework analyzes data locally using edge computing, lowering latency and providing real-time diagnostics. Federated learning keeps confidential patient data on local devices while enabling collaborative model training over edge nodes. To demonstrate the pros and cons of decentralized machine learning, federated learning models are bench marked against centralized methods. This research shows that federated learning on edge networks revolutionize medical diagnostics by delivering a robust, scalable, and privacy-preserving MRI brain tumor prediction solution.