Pandemic outbreaks are crucial to handle, like the COVID-19 pandemic, spanning from 2020 to 2022 and affecting different countries worldwide, exposing vulnerabilities in social distancing measures during the pandemic's peak. The study presents a fertile, deep learning model designed to enhance future pandemic prevention strategies by recognizing the multifaceted challenges of lockdowns, such as economic adversity, disrupted daily routines, and international trading halts. Our model leverages a comprehensive dataset encompassing demographic information on pandemic dynamics to identify regions and communities susceptible to lapses in social distancing. Crucially, our model goes beyond the constraints of lockdowns by offering real-time monitoring and alerting capabilities. It provides early warnings to health authorities, policymakers, and the public, and these alerts empower decision-makers to implement targeted interventions. This research heralds a transformative approach to pandemic preparedness, harnessing the potential of deep learning to protect vulnerable populations and prevent future outbreaks worldwide.