The increasing prevalence of malware propagation in IoT networks requires the development of efficient and adaptive mitigation strategies. Classical approaches based on optimal control theory, such as Pontryagin’s Maximum Principle (PMP), provide mathematically optimal solutions, but require solving a complex two-point boundary value problem. Recent advances in machine learning have introduced Neural Ordinary Differential Equations (ODEs) as a new alternative, allowing learning of non-linear control policies through gradient-based optimization. In this work, we apply neural ODEs to solve an optimal control problem in a modified SIR model of malware propagation, incorporating two control functions: \(u_1(t)\) , which reduces transmission, and \(u_2(t)\) , which enhances recovery. We compare this approach with a PMP-based control solution obtained using the shooting method. Our results show that both methods effectively reduce the infection peak, but exhibit different behaviours in the timing and magnitude of interventions. This study highlights the advantages and limitations of both approaches, and provides a foundation for hybrid control strategies that combine machine learning with traditional optimal control techniques for malware mitigation.