In India, conventional techniques for forecasting crop yield often overlook the complex interactions between climate factors, soil conditions, and crop varieties. However, deep learning presents a promising solution by utilizing artificial neural networks to analyze massive volumes of data and uncover hidden patterns. This study investigates deep learning models' efficacy in forecasting crop yields across different agroecosystems in India. Covering several deep learning architectures, especially recurrent and convolutional neural networks, was essential to assess their ability to learn from historical yield data, meteorological data, soil parameters, and remote sensing images.