As the human population grows, innovations such as virtual patients, vaccines, biotechnological machines for medical production, and microneedles provide effective solutions to global health crises. Deploying smart robots and support systems can help governments reduce public spending on future respiratory viruses, such as a NextGen Respiratory Virus or NeoCorona Virus. This paper presents a robot-based finite-time optimal control model to combat a hypothetical infectious disease, Pandemic-X, with the potential to cause a future global pandemic in unplanned human populations. The robotic system is specifically optimised for Pandemic-X, integrating saturated incidence rates, robotic control, and vaccination to enhance public health emergency responses. Our approach consolidates optimal control strategies, including vaccination and Computational Internet of Things robotics (CIoTR), in the post-COVID-19 era. Two innovative strategies are proposed: Pontryagin stochastic optimisation for managing COVID-19 spread and a CIoTR-based control strategy. The combined approach applies optimal control theory to maximise the susceptible population S(t), and treated/recovered population T(t), while minimising the exposed E(t), asymptomatically infected A(t), and symptomatically infected I(t), groups. On top of this, the robot operates in three optimised power modes: i) ‘super-active’ for high-computation edge inferencing and active vaccination, ii) ‘moderate’ for balanced fallback operations, and ‘sleep’ for dynamic idle states. Results show strong alignment between real and simulated data in vaccination rates, infection reduction, and hospitalisation trends. These findings demonstrate that optimal control strategies, particularly those leveraging robotic systems, can effectively manage the spread and impact of Pandemic-X, reduce healthcare burdens, and minimise transmission risk.