The significance of infrastructure development within households is incomplete without the implementation of an efficient home security solution. Many existing security systems grapple with challenges such as lack of seamless integration, diminished accuracy, and a user-unfriendly experience, often compounded by high costs. Thus, the need for an innovative and effective approach to enhance the safety and security of homes becomes imperative. A groundbreaking solution to address these concerns is the integration of smart IoT-based touchless doorbells with face-detection capabilities. This research endeavors to propose a touchless doorbell system incorporating a face recognition model developed using OpenCV and a Haar cascade classifier. The integration of this system with an Arduino UNO further enhances its functionality. Through this innovative approach, a remarkable improvement in efficiency and accuracy, reaching approximately 93%, has been achieved. This, in turn, contributes to the establishment of a highly reliable and secure home security system. A noteworthy aspect of this research is its positive impact on the safety of visually impaired individuals. The touchless doorbell system has been augmented with features from the Google Text-to-Speech (gTTS) library, providing audio-related functionalities. This not only bolsters the overall accessibility of the security system but also ensures that visually impaired individuals can interact and navigate with the proposed home security system, thereby fostering inclusivity in the realm of home security solutions.