This research presents the design and implementation of a novel device aimed at enhancing the safety of visually impaired individuals during travel through sound localization and identification. The prospect of diminished productivity resulting from varying degrees of visual impairment underscores the urgency of addressing blindness. This imperative extends beyond mere poverty alleviation; it serves as a gateway to female empowerment and societal integration. Leveraging Raspberry Pi 4 microcontroller and a suite of Python libraries including NumPy, Sound device, SciPy, TensorFlow, and pyttsx3, the device identifies sound sources and their directional orientation, alerting users in real-time. Methodologically, the project involves loading necessary data and parameters onto the microcontroller, integrating pre-trained sound classification models, and initializing a speech synthesizer for user notification. Key functions include angle calculation for sound direction determination, audio capture for data acquisition, and sound localization and identification through signal processing and machine learning. This research signifies a significant advancement in assistive technology, ensuring safer mobility for visually impaired individuals through innovative sound-based navigation systems.