The quality, accessibility, and stability of the business environment directly affect the competitiveness and growth of enterprises. Therefore, this study aims to classify Mongolia’s business environment using machine learning techniques and identify each cluster’s specific characteristics and key influencing factors. The research utilizes data from 4,073 enterprises operating in Mongolia as of 2020, comprising 104 variables related to the business environment. Since the original dataset contained many missing values, imputation was necessary. Mode and K-Nearest Neighbors (KNN) imputation methods—commonly used in research—were tested. Their mean absolute errors (MAE) were evaluated against the original data, and KNN (MAE = 0.0119) demonstrated higher accuracy than Mode (MAE = 0.0438). As a result, the KNN-imputed dataset was used for further analysis. Five clustering algorithms were applied to group the data, and their performance was assessed using three metrics: Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Score. Among these, the HDBSCAN model yielded the best results (Silhouette Score = 0.435, DB Index = 0.409), and after removing 1,051 enterprises classified as noise, the remaining 2,852 enterprises were grouped into three clusters. Additionally, Random Forest classification and SHAP analysis were used to identify the most influential features in cluster formation. The study showed that bank interest rates, the effectiveness of government policies, and household living standards had the most significant impact on cluster differentiation. By identifying these key drivers, this study provides scientifically grounded insights that support evidence-based decision-making for policymakers and researchers seeking to improve the business environment.