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Stock price prediction serves as a fundamental tool for informed financial decision- making This project presents a web-based platform for different users that uses Recurrent Neural Networks (RNNs) to predict stock price. The platform integrates historical stock data encompassing opening and closing prices, high and low prices, and trading volume. A comprehensive preprocessing pipeline is established, encompassing data cleaning, normalization, and feature selection. RNN architectures, including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUS), are employed to capture intricate temporal patterns essential for accurate prediction. The trained RNN is a model which incorporated into a web interface, allowing users to input stock symbols and obtain real-time predictions. The system's performance is evaluated by using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). This web-based application demonstrates the efficacy of RNNs in stock price prediction and provides an accessible and interactive tool for investors and traders to enhance their financial strategies.