Hallucinations in AI-generated content pose a significant challenge to the reliability and trustworthiness of advanced language models, particularly as they become increasingly integrated into decision-making processes across various domains. The novel concept of employing Monte Carlo simulations on token probabilities offers a robust framework for detecting hallucinations, enhancing the accuracy and reliability of AIgenerated content. The methodology involved generating multiple outputs for diverse prompts, calculating token probabilities, and performing Monte Carlo simulations to identify low-probability tokens indicative of hallucinations. Experimental results revealed varying frequencies of hallucinations across different domains, with everyday scenarios presenting the highest challenge. The probabilistic framework enabled a detailed analysis of the LLM's outputs, providing insights into its decision-making process and highlighting the efficacy of the Monte Carlo approach. The study's findings underscore the importance of refining LLMs to improve their performance and reliability in real-world applications, contributing to the ongoing efforts to mitigate hallucinations in AI-generated content.