The COVID-19 pandemic underscored the critical need for intelligent strategies to combat viral transmission. This research unveils “TriSafeGuard,” an innovative, integrated framework designed to oversee essential preventive measures, amalgamating sophisticated deep learning techniques with contactless temperature sensing. Our first approach involves a unique methodology utilizing Stacked Auto Encoder (SAE), which combines Principal Component Analysis (PCA) and Depth-wise Separable Convolutional Neural Network (DWSC-NN). This model is adept at identifying not only the presence of masks on individuals but also assessing their proper positioning. Such a technique achieved an impressive accuracy and F1 score of 94.16% and 96.009%, respectively. Next, we tapped into the capabilities of the YOLOv7 architecture for object detection to measure distances between individuals, ensuring adherence to social distancing norms. Utilizing Manhattan distance calculations in high-dimensional spaces, our framework can accurately evaluate the proximity of individuals within crowds. This application of YOLOv7 showcases the robustness of our proposed Social Distance Detection (SDD) technique in effectively discerning safe distances in diverse scenarios. Lastly, integrating a Raspberry pi 4 with an MLX90614 sensor, our setup ensures non-invasive temperature monitoring, forming a critical line of defense against identifying potentially symptomatic individuals. Taken together, these elements culminate in a holistic solution crucial not just for the prevailing pandemic but also as a preparative measure for potential future outbreaks. This paper exemplifies how a synergistic blend of technology can pave the way for a safer, fortified future.