The rapid expansion of ride-hailing services has led to increased customer interactions, requiring scalable and efficient support systems. Traditional human-operated customer service models struggle to meet demand, leading to slow response times, inconsistent service quality, and high operational costs. Automating customer support through artificial intelligence (AI), natural language processing (NLP), machine learning (ML), and predictive analytics enhances operational efficiency, improves response accuracy, and elevates customer satisfaction. This paper explores methods for automating customer support in ride-hailing, covering chatbot implementation, automated dispute resolution, AI-driven sentiment analysis, predictive analytics, and self-service tools. Case studies from Uber and Lyft illustrate best practices and the real-world impact of automation. The study also discusses challenges, ethical considerations, and future developments in AI-powered ride-hailing support.