Today, Machine Learning significantly enhances the decision-making processes across all fields. Agriculture is one such field where data driven approaches can help farmers and stakeholders make informed decisions regarding soil management, nutrient applications, crop selection, plant disease detection, and many such avenues. This paper presents a novel approach to integrating soil health data with cover crop attributes to facilitate informed crop planning. Leveraging machine learning methodologies, we propose a method to map soil health datasets with cover crop attributes. The process involves creating a mapping dictionary based on crop type and growing period, adjusting salinity tolerance values based on pH levels, and merging relevant attributes. The developed algorithm ensures robustness by implementing error handling mechanisms and data validation checks. Furthermore, the code is optimized for efficiency to handle large datasets efficiently. The effectiveness of the proposed method is demonstrated through a case study involving real-world soil health and cover crop datasets. Our mission is to transform the farming practices from an inorganic to organic pattern with the aim to rejuvenate the soil health.