In the Internet of Things (IoT) environment, a Distributed Denial-of-Service (DDoS) attack in the network causes poor performance and resource-limited issues to users. Existing systems do not provide real-time adaptability, leading to delayed mitigation. Also, centralized storage systems suffer from breaches and tampering. To tackle these issues, a secure and intelligent IoT DDoS detection and mitigation framework is presented that utilizes hybrid encryption, blockchain storage, ensemble deep learning (DL), and reinforcement learning (RL) to improve the accuracy, security, and efficiency of IoT networks against several cyber-attacks. The developed technique collects data from a dataset and pre-processes it for handling missing values and normalizes it for further analysis. Secondly, a hybrid encryption method combining Homomorphic Encryption (HE) and ChaCha20 is adopted for data encryption with optimal key selection using Dingo Optimizer (DOX). Then, the encrypted data is securely stored in blockchain through off-chain storage and on-chain hash storage to ensure data integrity and tamper-proof security. DDoS attack detection is performed using an ensemble model called TriGuard-Net that combines AlexNet, LSTM, and PSPNet, with optimizing hyperparameters using Fire Hawks Optimizer (FHO). Finally, an RL-based mitigation system using Deep Q-Network (DQN) helps in real-time attack mitigation and enhances IoT security. Experimental results reveal that the presented model offers superior performance by achieving an accuracy of 99%, a kappa score of 98%, an R2 Score of 97%, an MCC of 98%, a Jaccard Score of 98%, and a Hamming Loss of 0.006, thereby outperforming other current models.