Abstract

This study investigates the central role of probability in assessing and predicting risk and provides evidence to its applicability in insurance, engineering, meteorology, economics, and complex decision-making. By exploring probabilistic methods and their impact on the accuracy of predictions, the study shows how an integration of advanced tools like machine learning, big data analytics, and Bayesian models improves precision across all domains. Using data from 2000–2023, regression analyses validate five hypotheses, confirming significant improvements in risk estimation and prediction reliability. The findings underline the theoretical and practical importance of probability, bridging critical gaps in long-term model efficacy, risk mitigation strategies, and advanced tool integration.