The advancement of technology in the automotive industry has led to the emergence of smart cities where vehicles are interconnected through communication networks. This inter-vehicle communication (IVC) is crucial for enabling various intelligent transportation system (ITS) applications, such as traffic management, collision avoidance, and autonomous driving. The wireless communication for IVC exposes the system to security threats, including intrusion attacks. This paper presents intrusion detection techniques for IVC in smart cities, where KNN provides a more significant outcome than linear regression, logistic regression, and linear support vector machine classifiers. It finds the securing IVC and the different types of intrusion attacks. This approach additionally provides an overview of the existing intrusion detection systems (IDS) and their mechanisms for detecting and mitigating attacks in inter-vehicular communication. The paper illustrates an intrusion detection approach that leverages machine learning algorithms to analyze the network traffic and detect anomalies indicative of intrusion attempts. The proposed approach outperforms intrusion detection, where the achieved accuracy is  98.3%.