Integrating Identity Access Management (IAM) with simple machine Learning (ML) models presents a likely set about enhancing data integrity in cloud-based fleet management applications. This contemplates exploring IAM systems' carrying out to secure telematics data, ensuring unrefined access controls and assay-mark mechanisms. By leveraging ML models, we aim to observe and mitigate potential surety threats, thereby maintaining the reliability and confidentiality of spiritualist dart data. The research encompasses various simulation reports and real-time scenarios, supported by relevant graphs, to illustrate the efficacy of the proposed solution. Furthermore, we hash out the challenges encountered during the integration process and propose viable strategies to overcome them, ensuring a secure and efficient dart management system.