In the field of assistive technology (AT), individuals with visual impairments (VI) and speech impairments (SI) often encounter significant barriers to effective daily communication and social participation. This work presents the design and evaluation of an AI-powered integrated communication system aimed at bridging these gaps. The proposed solution combines multimodal interfaces that include voice commands, with real-time text and speech conversion to enhance user interaction. Adopting a user-centered design methodology, the system was iteratively refined through usability testing conducted in both controlled environments and real-world contexts. The results demonstrated notable improvements, with average task completion times decreasing from 45 to 28 seconds and communication success rates increasing from 76% to 91%. Additionally, user feedback emphasized clearer interactions, improved adaptability across contexts, and reduced frustration during use. Despite remaining challenges related to device compatibility, latency, and cost, the system demonstrated practical feasibility and significant value in supporting daily communication for users with VI and SI. This research provides a strong foundation for future developments, including multilingual capabilities, broader device support, and advanced AI features (e.g., predictive text and emotion recognition) to increase user autonomy and inclusion. Furthermore, the integration of customizable settings allows users to tailor the system according to their specific needs and preferences, enhancing accessibility and personal comfort. Continuous updates and machine learning algorithms ensure the system adapts dynamically to user behavior, improving efficiency over time. The scalability of the system suggests potential application beyond individual use, including educational and workplace environments, thereby promoting inclusivity on a larger scale.