Machine Learning (ML) is transforming crop production and management with its innovative solutions for optimizing farming operations, increasing output, and guaranteeing sustainable farming. Also, the integration of ML with Internet of Things (IoT) devices and remote sensing technologies enhances real-time decision-making and resource allocation for enhancing crop production. This work analyzes the popularity trends of Machine Learning Techniques in crop production from 1950 to 2024 and evaluates the suitability of different ML models for key agricultural tasks, such as yield forecasting, use of image classification for crop segregation, rainfall prediction, and crop rotation recommendations. By comparing model performance across the mentioned applications, this study highlights which ML techniques have historically been most effective for which types of applications. These “findings” serve as a guide for selecting the best-suited ML Technique for the requirement based on historical effectiveness. Results provide the groundwork for precision agriculture in the future by demonstrating that machine learning may enhance crop production sustainability and productivity.