As the aging population grows, effective healthcare communication becomes increasingly critical, particularly for older adults managing multimorbidity (multiple chronic conditions). Traditional methods often fail to engage this demographic, leading to misunderstandings, inefficient care coordination, and increased provider workload. This paper presents a generative AI-driven solution integrating a multimodal chatbot and an AI-enhanced provider dashboard to bridge this gap. The chatbot employs a hybrid architecture combining intent-driven logic with large language model (LLM)-powered natural language understanding (NLU) for safe, context-aware interactions, while the dashboard synthesizes patient–chatbot dialogues, extracting key insights like sentiment trends, discussion topics, and tone analysis to aid clinical decision-making. Additionally, an LLM-assisted virtual meeting room enables real-time transcription, patient history summarization, interactive querying of past interactions, and streamlining consultations. By leveraging conversational AI, real-time analytics, and AI-assisted care coordination, this scalable solution enhances accessibility, promotes independent living, and improves provider efficiency, offering a transformative approach to patient-centered healthcare for aging populations.