Agriculture stands at the core of our ability to sustain a global population; however, it faces a host of challenges: environmental change due to climate, resource shortages, and a growing requirement for higher productivity. Machine learning (ML) has demonstrably appeared as a powerful possibility for raising crop yields and promoting sustainable farming strategies in response to these challenges. Concerning crop yield, determinants include temperature, precipitation, and humidity, which rely on climate and soil characteristics, for example, texture, moisture and fertility together with agricultural methods including fertilization, irrigation, and tillage, along with biological forces from pollinators, pests, and diseases Together with pollution, topography, and water quality, and more, affect crop yields, thereby making crop productivity management even more challenging. The following is therefore a generalizable of how use of various machine learning models can be used for yielding prediction in Agricultural production. Within the sphere of supervised learning models, the families of algorithms that matter are linear regression, random forest, and support vector machines. A handful of other studies choose the unsupervised learning methods and deep learning how these algorithms including the CNN and RNN for the yield predicting. As well, the case for using remote sensing technology combined with geographic information systems (GIS) for the monitoring of crops is examined. Such tools seem to have proven their usefulness in guiding agricultural decision-making, based on these examples. Further, by the application of overall machine learning perspectives; factors such as water and nutrient management in crops are boosted. Last of all, the study focuses on the new development of artificial intelligence and machine learning in the agricultural field. Modern technologies and the identified areas of scholarship to enhance food production and productivity include among them technology such as Artificial Intelligence to increase crop production.