The modern agricultural landscape faces a lot of challenges, ranging from climate variability to market dynamics, all of which intricately influence the price of essential commodities like vegetables. As agriculture intersects with technology, the system stands at the intersection of machine learning—deep learning (ML-DL) and agro-economics. Drawing upon a comprehensive dataset that encompasses climatic variables, market prices, which is directly reflected on the price of the commodity. This research employs gradient boosted (GB) regression, decision tree (DT) regression, long short-term memory (LSTM) and Bidirectional LSTM (BLSTM) techniques to discern the intricate relationships between the factors such as humidity, precipitation, temperature, price to name a few and their impact on the vegetable ecosystem. This paper helps in prediction of tomato prices based on features like precipitation, humidity and temperature. Fluctuations in weather conditions, such as excessive rainfall, drought, or extreme temperatures, can affect crop yields and lead to fluctuations in prices. In response to this complex scenario, this paper presents research aimed at deciphering the influences on tomato prices using ML-DL algorithms. The model's development provides technological assistance for tomatoes marketplace tracking and advance notification, as well as guidelines to farmers to cultivate tomatoes crops. The research gives the glimpse of the different algorithms used together with the visualisation for each with the mean absolute error (MAE), R-squared (R2) and mean squared error (MSE). The performance of different algorithms was evaluated for predicting tomato prices: GB, DT, LSTM, and BLSTM. The LSTM model achieved an R2 score of 0.97, with a MSE 15.5, root MSE (RMSE) of 3.94 and MAE of 2.67. Similarly, the BLSTM model, attained an accuracy score of 0.96 with MSE of 13.82, RMSE of 3.72 and MAE of 2.47. In comparison, both the GB regression and DT regression models have accuracy score of 0.92 and 0.93, respectively. However, they exhibited higher MSE values of 25.17 and 27.1, respectively.