This project aims to develop a stock market price prediction system for the Colombo Stock Exchange (CSE) using candlestick chart images and deep learning techniques integrated with Explainable AI (XAI). Unlike traditional numerical forecasting models, this research focuses on visual patterns within candlestick charts to capture complex price movement trends. The system utilizes Convolutional Neural Networks (CNNs), specifically EfficientNetB7, to extract meaningful features from candlestick chart images. These extracted features are then fed into a Long Short-Term Memory (LSTM) model to perform sequential time-series forecasting and predict future stock prices, including open, high, low, and close values. Additionally, the system incorporates XAI methods to provide visual explanations for the model’s predictions, enhancing transparency and building investor trust. The ultimate goal is to offer an intelligent and interpretable decision support tool for investors and financial analysts, helping them understand not only the predicted outcomes but also the reasoning behind them. By combining image processing, deep learning, and explainability, this project bridges the gap between predictive accuracy and model interpretability in financial forecasting for the CSE.