The recent synergistic explosion of Artificial intelligence and the world of machines, in a bid to make them smarter entities as a result of the fourth industrial revolution, has resulted in the concept of chatbots which has evolved over the years and gained heightened attention for the sustainability of most human corporates. Organisations are increasingly utilising chatbots towards enhancing customer engagement through the process of agent based autonomous sensing, interaction, and enhanced service delivery. The current state of the art in the chatbot technology is such that the system lacks the ability to conduct text-sensing in a bid to acquire new information or learn from the external world autonomously. This has limited the current chatbot systems to being system controlled interactive agents hence, strongly limiting their functionalities and posing a question on the purported intelligence. In this research, an integrated framework that combines the functionalities and capabilities of a chatbot and machine learning was developed. The integrated system was designed to accept new text queries from the external world and imported to the knowledge base using the SQL syntax and MySQL software. The search engine and decision-making cluster was built in the Python coding environment with the learning process, solution adaptation and inference, anchored using Reinforcement machine learning approach. This mode of chatbot operation, with an interactive capacity is known as the mixed controlled system mode, with a viable human-machine system interaction. The smart chatbot was assessed for efficacy using performance metrics (response time, accuracy) and user experience (usability, satisfaction). The analysis further revealed that several self-governed chatbots deployed in most corporate organisations, are system-controlled and significantly constrained hence, lacking the ability to adapt or filter queries beyond their predefined database when users employ diverse phrasing or alternative terms in their interactions.