Accurate forecasting and effective risk assessment are critical components of modern business decision-making. With the rapid growth of data availability and computational power, machine learning (ML) has emerged as a powerful tool for improving predictive accuracy across diverse business domains. This study presents a cross-domain analysis of commonly used machine learning models for business forecasting and risk assessment, focusing on their applicability, performance, and limitations in different contexts. The research examines supervised learning models—including linear regression, decision trees, random forests, support vector machines, and neural networks—across financial forecasting, credit risk assessment, demand prediction, and operational risk management. Using secondary datasets and prior empirical findings, the study compares model performance based on prediction accuracy, interpretability, scalability, and robustness. The analysis highlights that while complex models such as neural networks and ensemble methods often achieve higher predictive accuracy, simpler models retain importance due to their transparency and ease of implementation. Furthermore, the study emphasizes that no single machine learning model is universally optimal; rather, model effectiveness depends on domain characteristics, data quality, and business objectives. The findings contribute to the growing literature on applied machine learning by offering a structured framework for selecting appropriate models across business domains. This research provides practical insights for managers, analysts, and policymakers seeking to integrate machine learning into forecasting and risk assessment processes while balancing performance and interpretability.

Machine Learning, Business Forecasting, Risk Assessment, Predictive Analytics, Cross-Domain Analysis

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