Indoor navigation poses significant challenges for those with visual impairments. To address this, an integrated system is proposed, aiming to empower visually impaired individuals by enhancing their indoor navigation and object recognition capabilities, ultimately promoting independence and safety. This innovative system amalgamates cutting-edge technologies such as IoT, deep learning, and machine learning. It incorporates IoT for real-time object detection, precise distance estimation, and collision avoidance features, ensuring users are well-informed about their surroundings. Moreover, the system employs a Wi-Fi-based indoor positioning system, combining machine learning algorithms and triangulation techniques to provide accurate indoor localization data, a critical component for effective navigation. To further enhance user experience, an audio assistant integrated into a mobile application leverages machine learning. This assistant delivers real-time guidance, object descriptions, and contextual information, enabling users to interact naturally with the system. This comprehensive research paper thoroughly explores the system's components, the underlying technologies that power it, and the results of comprehensive evaluations. It holds the promise of significantly improving the indoor navigation experience and quality of life for individuals with visual impairments.