To improve agricultural yield prediction, this study explores the use of transformer models in conjunction with convolutional neural networks (CNNs). The goal is to make crop production predictions more accurate and up-to-date by using a hybrid model that looks at geographical, climatic, soil, and socioeconomic data. This includes water and weather conditions, soil pH, degree days, and the traits of each crop. While the CNN part is great at deducing crop health-related spatial characteristics from remote sensing data, the Transformer part is excellent at capturing temporal relationships from time-series data, such as weather and market dynamics. According to early findings, this hybrid strategy provides more accurate forecasts than conventional methods. Improved decision-making, less waste, and increased output are just a few ways in which this approach may revolutionise precision agriculture. In the end, it helps make the world’s food supply system more robust and long-lasting.