Objectives: This paper investigates soil microbial biomass and its key function in soil fertility, nutrient cycling, and resilience of the ecosystem. It aims at improving the accuracy of the microbial biomass estimations through the application of machine learning methods. Methods: Various evaluation approaches to microbial biomass were reviewed, comprising those that analyze traditional soil samples as well as ones that include environmental parameters. Models based on machine learning such as Random Forest, Support Vector Machine, Gradient Boosting, Neural Networks, and Naïve Bayes were used for microbial biomass prediction by means of a diverse set of soil and environmental factors and characteristics. Findings: The study proved that machine learning techniques substantially enhance the accuracy of predicting soil microbial biomass dynamics. The Random Forest method, in particular, showed a high prediction performance with low error metrics. The application of machine learning models with soil and environmental data provides a strong framework for soil health assessment and management. Novelty: This research highlights the novel application of machine learning techniques in the field of soil science and their capacities to better traditional microbial biomass assessments. Besides, it coarsely points out the relevance of sustainable land management practices, such as reduced tillage and organic amendment application, for soil health and for the enhancement of microbial diversity.