Conversational AI has rapidly evolved, impacting various domains such as customer support, healthcare, and education. Evaluating these models is critical for ensuring their effectiveness, accuracy, and user satisfaction. This research paper explores various evaluation methodologies, including qualitative and quantitative assessments, benchmarking approaches, and real-world application testing. We present a comparative analysis of leading conversational AI models using established metrics and provide experimental results to validate our findings. The study also includes code snippets demonstrating evaluation techniques and discusses the challenges and future directions in assessing conversational AI.