Traditional Agriculture has always involved the use of data. However, modern data challenges have become increasingly complex. As a result, data mining processes are now employed to construct machine learning models that analyze and extract valuable insights from this data. In the context of agriculture, data mining plays a crucial role in predicting crop yields, forecasting climate patterns and rainfall, optimizing seed and soil selections, and enhancing overall crop production efficiency. Agriculture serves as a cornerstone of India’s economic framework. Looking ahead to the next generation, the global population is projected to continue its growth. By 2050, the Earth will be home to an additional 9 billion people. Consequently, there is a pressing need to increase food production by 70%, all while utilizing less land. A common issue faced by numerous farmers is the challenge of selecting the appropriate crops based on their soil composition and local environment. This predicament leads to crop diseases, elevated production costs, and significant declines in productivity. Precision agriculture has emerged as a solution to this problem. Precision agriculture, rooted in traditional farming practices, leverages soil analysis, climate conditions, and temperature data to offer refined guidance on crop selection. This approach empowers farmers to enhance their output and productivity. In the context of this paper, we proposed a solution to address this challenge: the implementation of a recommendation system using a Random Forest classifier, which falls under the umbrella of Ensemble techniques. Additionally, the model is further refined by training it using Artificial Neural Network (ANN), employing the K-Fold Cross-validation technique through Keras. This strategy aims to augment the accuracy of model training, resulting in improved recommendations and outcomes.