Falling often causes deadly conditions such as unconsciousness and related injuries among the elderly population if failing provided with aid and caretakers nearby. In this context, an automatic fall monitoring system gains its popularity by solving the problem with immediate prompting, thereby allowing the caretakers and other persons to get activated with an alarm message. It assists older adults in living without fear of falling and being independent in society. In recent decades, vision-based fall monitoring receiving attention among research communities for its diversified features. It helps identify the human in the intended regions, and by using the collected phenomenon from the area, it trains the fall recognition classifiers. Besides, human detection errors and lack of massive-scale datasets make the vision-based fall monitoring face challenges like robustness and efficiency in performing generalization to invisible regions. Hence a robust learning and classification system is reasonably needed to combat the challenges. In this proposed system, automatic fall detection using deep learning is modeled using RGB images gathered from the single-camera source. More significantly, it determines the sensitive details that prevailed in the original images and ensures privacy, widely considered for safety and protection. Various experiments are carried out using real-time world fall data sets. The results show that the system enhances fall recognition awareness and achieves a high F-Score by performing high accurate fall detection from real-world environments.