With the growing demand for food production across the globe, combating population boom, climate change, and achieving sustainability has not been an easy feat. As the world moves toward securing sustainability, conventional strategies prove unsatisfactory due to use of large-scale inputs, high production costs, soil and water degradation, health hazards, and inefficiency. Therefore, the need for a precise, climate-smart agricultural system can be met by innovative predictive analytical techniques. They can forecast crop health and yields making use of past and present weather, plant and soil parameters. Predictive analytics uses techniques like remote sensing, GIS, machine learning models, Internet of Things (IoT), crop simulation models, vegetation indices (NDVI), etc., that facilitate precise decision-making by generating future projections. ‘Precision agriculture’ is one such type that makes use of this forecasting technique. Thereby, it decreases production cost, improves input allocation and crop yields by several folds. Although lack of experts, quality data, infrastructure, scalability, awareness, etc., can be a constraint to its implementation, curbing these can promote ecological sustainability and boost productivity. This chapter deals with the evolving predictive analytical techniques in agriculture, their problems and solutions and future scopes.