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Agricultural price forecasting is a critical challenge in ensuring the financial stability of farmers and the overall efficiency of the food supply chain. Among various crops, tomatoes are highly sensitive to weather fluctuations and seasonal changes, leading to frequent price volatility. This unpredictability can severely impact both small-scale and large-scale stakeholders in the agriculture sector. To address this issue, the project focuses on developing a robust forecasting framework for tomato prices by leveraging historical data and advanced predictive algorithms.The methodology integrates both traditional statistical approaches and modern deep learning models. The Auto-Regressive Integrated Moving Average (ARIMA) model is used to establish a baseline for time series analysis, while Long Short-Term Memory (LSTM) networks are employed for capturing complex temporal dependencies in the data. The dataset includes various attributes such as date, minimum/maximum/average prices, market indicators, and seasonal factors. Preprocessing steps such as handling missing values, feature scaling, and seasonal trend extraction are carried out to enhance model accuracy. The models are evaluated using Mean Squared Error (MSE) and visual comparisons between predicted and actual values.The results demonstrate that the LSTM model outperforms ARIMA in capturing non-linear patterns and seasonal variations in tomato price trends. The proposed forecasting system can serve as a valuable tool for farmers, traders, and policymakers to make data-driven decisions.