Healthcare service efficiency has become a critical factor in ensuring patient satisfaction in modern hospitals. One of the major issues faced by multi-speciality hospitals is increased patient waiting time, which affects the quality of care and overall experience. This study focuses on analyzing the key factors responsible for delays in patient services and explores the role of Artificial Intelligence (AI) in reducing waiting time. The research is conducted in a multi-speciality hospital setting, where data is collected from patients through structured questionnaires. Statistical tools such as ANOVA and Chi-square are used to identify the relationship between different variables influencing waiting time. The study reveals that factors like poor scheduling, limited resources, and workflow inefficiencies are the main causes of delays. Furthermore, the study highlights how AI-based techniques such as predictive analytics and automated scheduling can improve hospital operations. These technologies help in better resource management and faster decision-making, thereby reducing patient waiting time. The findings conclude that implementing AI in hospital management systems can significantly improve operational efficiency and enhance patient satisfaction. The study provides valuable suggestions for hospitals to adopt advanced technologies for better healthcare service delivery.