The increasing number of solitary deaths among elderly individuals living alone is a critical social and public health concern. Traditional monitoring systems, such as tracking water or electricity consumption, are indirect, reactive, and often delayed in detecting crises. These methods lack real-time responsiveness, emotional engagement, and cognitive state assessment. This research proposes an AI-driven, conversational monitoring system leveraging Large Language Models (LLMs) for proactive, real-time interaction with elderly individuals. Unlike existing monitoring solutions, our system uniquely integrates empathetic dialogue, daily check-ins, and emotional context recognition through natural conversation. It also includes features such as personalized birthday reminders and socially engaging prompts to enhance companionship and emotional support. By applying generative AI to conversation data, the system identifies distress signals and behavioral anomalies, enabling early intervention in medical or psychological emergencies. Additionally, it provides continuous well-being insights for caregivers and policymakers through aggregated interaction trends. Ultimately, this approach not only improves the quality of life for elderly individuals but also represents a paradigm shift in AI-powered, community-based elderly care.