Efficient healthcare administration is important for maximizing hospital operations and enhancing patient care. Conventional hospital management systems are plagued by scheduling inefficiencies, administrative loads, and inefficient resource utilization. This study presents an AI system for healthcare management that integrates deep learning, NLP, and genetic algorithms for optimizing operations. DNN predictive model identifies 94.5% no-shows with correct predictions, reducing 37% less schedule gaps, while an NLP document system decreases processing time by 42% and raises accuracy to 89.3%. Further, an AI-resource management with Genetic Algorithms maximizes hospital planning effectiveness to 92% and saves 18% in cost. AI is evaluated through a comparative study against traditional systems for scheduling, documentation, and resource utilization. Despite facing issues such as privacy concerns as well as raised implementation fees, the system improves decision-making, scalability, and cost-sustainability. This study vouches for the transformative ability of AI to govern healthcare and develop data-centric, effective, and cost-efficient hospitals.