The COVID-19 pandemic has triggered a serious global health crisis, with profound impacts on public health, the economy, and society in Malaysia and worldwide. This pandemic has reinforced the need for more effective, integrated, and evidence-based disease control strategies. This research aims to develop a comprehensive epidemiological mathematical model based on the SEIR framework, using local data to understand the epidemiological trends of COVID-19 in Malaysia and assess the effectiveness of various disease control interventions. The study found that during the second wave, the transmission rate of COVID-19 was approximately 8.00373 per day, and the latency period was 5.7 days. Additionally, immunity waned approximately 167 days after vaccination. The emergence of new variants was found to increase the transmission rate and shorten the latency period. Interventions such as screening and contact tracing, self-control, movement control, and vaccination had significant effects on reducing disease transmission, with strict movement control providing the most significant reduction. The model also showed that vaccination could reduce susceptibility to infection depending on the number of doses received. This modeling allows for the development of an optimal control model using optimal control theory, which indicates that a combination of controls at the highest level is necessary for maximum disease control. Overall, the study demonstrates that an SEIR-based mathematical model, combined with optimal control theory, is a vital tool for planning more effective public health interventions and providing scientific evidence for controlling future pandemics, especially in the face of mutations and new variants. The findings of this study are expected to contribute significantly to the field of public health and infectious disease management in Malaysia, providing precise guidance for public health planning while strengthening the resilience of the national health system in facing infectious disease threats.