The rise of internet purchasing in the last few years is quite remarkable. Despite this growth, not all aspects of internet buying have been perfected. For example, unlike in physical stores, you can't haggle with vendors about prices. A chatbot for product negotiations is now live. Customers are able to acquire a good deal on product(s) with the help of the chatbot. The approach might end up hurting either the goods seller or the customer's budget, as it affects a lot of different parts of online buying. We have devised an algorithm that, in conjunction with the forecast of previously accessible data, can offer a price in order to circumvent such scenarios. Using unrelated data elements or qualities or techniques that aren't a good fit for a certain dataset might reduce the accuracy of price prediction. In light of the fact that erroneous product price predictions may lead to significant financial losses for online retailers, these companies avoid relying only on price prediction algorithms. When data becomes too large or when a characteristic that was relied on the model's prediction becomes unavailable, certain models can fail. Then, in order to keep the model's accuracy and dependability intact, such modifications must be handled. We have made an effort to address some of these concerns in our chatbot system.