The rapid expansion of ride-hailing services has led to significant challenges in maintaining efficient and scalable customer support systems. Ensuring platform reliability is critical to handling surges in customer support requests, particularly during peak hours, major events, and service disruptions. This paper explores the methodologies, technologies, and frameworks that enable ride-hailing platforms to scale customer support while maintaining service reliability. Case studies from leading ride-hailing companies, including Lyft, Uber, Grab, Didi, and Bolt, demonstrate best practices in implementing artificial intelligence (AI), machine learning (ML), predictive analytics, and automation to optimize support operations. The paper further examines the impact of surge pricing models on support demand, the role of AI-powered chatbots in enhancing customer experience, and strategies for managing workforce scalability. A discussion on infrastructure resilience and disaster recovery strategies provides insights into maintaining operational efficiency under high-demand conditions. The research concludes with future trends in AI-driven customer support and recommendations for ensuring sustained scalability and reliability in the ride-hailing industry.