Incorporating a MATLAB-based Spatial-Temporal SIR Model, this investigation provides an in-depth framework to unravel the complexities of infectious disease dynamics, focusing on the interplay between spatial dispersion and temporal evolution of epidemics. The study meticulously examines the pivotal roles of transmission and recovery rates, alongside spatial spread patterns, leveraging visual outputs and detailed parameter analysis to illuminate the pathways of disease transmission and the significant influence of various determinants on epidemic progression. A distinguishing feature of this model is its adaptability. Moreover, this research delves into the treatment-based compartmental model, adding a novel dimension to our understanding by segmenting the population based on treatment stages, thereby offering a nuanced perspective on disease management strategies. The integration of spatial-temporal dynamics with treatment phases gives invaluable resource for health policymakers. The objectives of this study are multifaceted, aiming not only to forecast infectious disease spread with a refined compartmental model but also to propose robust quarantine and isolation policies, alongside strategies to enhance immunity against sudden outbreaks. Through comprehensive modelling and simulation in MATLAB, this research articulates a strategic framework for pre-emptive actions and intervention strategies. The findings of this research have the potential to inform evidence-based decision-making and improve preparedness for future public health emergencies.