Micro, Small, and Medium Enterprises (MSMEs) play a vital role in driving economic growth; however, their production activities frequently face uncertainty in achieving predetermined targets. Such uncertainty arises from fluctuating market demand, delays in raw material supply, labor limitations, variations in processing time, and other technical constraints. Conventional deterministic production planning methods often fail to capture these real-world risks and variations, leading to less accurate and suboptimal decisions. Therefore, a more adaptive analytical approach that incorporates probability and uncertainty is required. This study aims to analyze the probability of achieving MSME production targets using the Monte Carlo Simulation method. This method models random production conditions by generating data based on probability distributions derived from historical records. Simulations are repeated through numerous iterations to estimate possible variations in production output and measure the likelihood of meeting targets. The results indicate that Monte Carlo simulation provides more realistic and comprehensive production forecasts compared to traditional planning approaches. By understanding both the probability of success and potential risks, MSMEs can design adaptive strategies, optimize resource allocation, manage inventory more effectively, and improve overall production planning accuracy to ensure long-term business sustainability in a dynamic environment.