Classification of soils as one of the necessary practices serves as a basis to the current patterns in farming as regards the crops to produce or the rates of production carried out in agriculture as well as the ecological concerns of the environment. Traditional practices in classifying soil includes the chemical properties inclusive of pH of the soil, with physical features that includes texture and nutrients in it, though the research that has been done has indicated that the microbiome of the soil determines the fertility level of the soil. As a result, the works under discussion outline an AI system that incorporates microbiome sequencing via 16S rRNA, as well as the application of a machine learning algorithm to identify various types of soils and recommend relevant agricultural crops. This integrates Random Forest/XGBoost models for classification of soils and crop recommendation services. By integrating microbiome sequencing with machine learning, a new tool increases the prediction accuracy by 18% over traditional practices of soil classification. This study enhances crop yield prediction and positive advancement in agriculture by integrating soil analysis of biology with artificial intelligence analytical tools.