<p>The internet of vehicles (IoV) has revolutionized transportation by enabling real-time communication among vehicles, infrastructure, and cloud-based services. However, the increasing interconnectivity in IoV networks exposes them to a wide range of cyber threats, necessitating robust intrusion detection mechanisms. Blockchain technology has emerged as a powerful tool for enhancing the security and privacy of sensitive data by providing immutable and tamper-proof storage. However, a critical security gap remains: data remains vulnerable to attacks before being securely stored on the blockchain, making it susceptible to malicious exploitation. To address this issue, we propose SecureAlert, a novel blockchain-integrated intrusion detection system (IDS) that leverages a hybrid CNN-LSTM model to enhance IoV security. The proposed system combines the feature extraction capabilities of convolutional neural networks (CNNs) with the sequential learning strength of long short-term memory (LSTM) networks to detect cyber threats in real-time. Additionally, blockchain technology is integrated to ensure tamper-proof storage of intrusion alerts, mitigating the risk of unauthorized data manipulation. Particularly, this paper highlights the potential of combining deep learning with blockchain for securing next-generation IoV networks while addressing pre-storage data vulnerabilities. SecureAlert is evaluated using benchmark datasets (CIC-IDS2017, CSE-CIC-IDS2018, and CIC-DDoS2019), achieving high detection accuracy while effectively handling imbalanced data distributions through advanced preprocessing techniques. Comparative analysis with state-of-the-art intrusion detection methods demonstrates the superior performance of SecureAlert in terms of accuracy, recall, and false positive rates.</p>