The autonomous migration capability of mobile agents makes them highly susceptible to various security threats during their movement across distributed platforms. Consequently, ensuring the security of both mobile agents and their execution environments has become a critical concern in distributed network systems. However, most existing protection mechanisms in mobile agent–based paradigms primarily focus on safeguarding the host platforms, while comparatively less attention has been devoted to protecting the agents themselves. To address this limitation, this paper proposes a hybrid authentication model for secure mobile agent migration that integrates a mathematical key management technique based on Euler’s Theorem with a machine learning mechanism for enhanced agent security. The proposed approach enables secure hopping of mobile agents between different platforms through a double encryption mechanism applied to the secret key shared among n mobile agents. Furthermore, the proposed model incorporates machine learning to strengthen authentication and detect potential malicious behavior during mobile agent migration. The proposed technique is evaluated and compared with existing mobile agent security approaches. Experimental results demonstrate that the proposed model outperforms conventional methods in terms of improved agent security and reduced execution time. To validate the effectiveness of the proposed approach, a comparative analysis is conducted with existing key management schemes based on the Chinese Remainder Theorem (CRT) and Shamir’s Secret Sharing. The Outcomes show that the time complexity of the proposed method is 0.0071 seconds, which is significantly lower than that of the existing techniques for the same number of mobile agents thereby demonstrating the efficiency and practicality of the proposed hybrid authentication model.