Conversational AI Revolution: A Comparative Review of Machine Learning Algorithms in Chatbot Evolution

Abstract

Chatterbots, also known as chatbots, have become essential for improving human-computer interaction in a number of fields, including e-commerce, healthcare, education, and customer support. From rule-based systems like ELIZA to contemporary AI-driven solutions employing modern machine learning (ML) techniques, this review paper examines the development of chatbots. It highlights how ML technologies, such as decision trees (DT), support vector machines (SVM), linear regression, and natural language processing (NLP), can be used to build chatbots that are more context-aware, responsive, and adaptive. The paper highlights important advances including deep learning, multimodal capabilities, and continuous learning mechanisms by looking at recent advancements and the mathematical models that support these techniques. These developments have driven an increasing support for chatbots by allowing them to provide personalized interactions, enhance accessibility, and reduce repetitive tasks. In order to open the door for further study and applications, this paper aims to bring light on the challenges and the efficacy of using ML into chatbot building.