We investigate a branched artificial neural network (ANN) feedback controller for mitigating outbreaks in compartmental epidemic models. The architecture couples a shared trunk with two specialized branches and employs Soboleva–modified hyperbolic tangent (SMHT) activations to approximate the shape and boundedness of analytic control laws, yielding smooth, non–bang–bang signals suited to implementable interventions. The network is trained offline in supervised fashion on synthetic SIR trajectories labelled by a control that steers the infected population toward a low terminal target over a fixed horizon. On unseen SIR scenarios, the learned policy lowers peak prevalence and shortens outbreak duration relative to uncontrolled dynamics. When compared against simple baselines; however, the ANN achieves these outcomes with markedly smoother profiles and reduced actuation effort (time integral of the control), a property desirable for practice. Without retraining, the controller transfers to SEIR and retains qualitative benefits consistent with partial observability induced by the latent exposed class. We evaluate our suggested controller against conventional neural network baselines through ablation studies and robustness tests incorporating multiplicative process noise. The results demonstrate that our branched architecture reduces the attack size and peak infection with a comparable control effort. Importantly, the controller exhibits smooth, bounded actuation signals even when subjected to significant uncertainty. We discuss limitations and outline extensions: identification from data, observer design for latent/noisy states, explicit resource and rate constraints, and online adaptation under distribution shift.