In today’s data-driven and modern world, data is recognized as one of the most important informational resources that can be utilized in making intelligent and optimized decisions. The accounting profession, in the age of the information explosion, faces an unprecedented volume of financial and non-financial data. This study, employing a systematic literature review method, examines modern predictive models and their transformative applications across various areas of accounting. Predictive models are key tools in data science, capable of forecasting future events and simulating behaviors by using historical data and statistical or machine learning methods. These models are applied across numerous industries, including healthcare, business, finance, education, and even weather forecasting. The findings show that algorithms such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and particularly deep learning and Large Language Models (LLMs), demonstrate a high capacity for predicting bankruptcy, Financial Fraud Detection (FFD), credit risk, asset valuation, and even analyzing accounting texts. By utilizing big data (a combination of structured financial data, news, social media, etc.), these models have significantly enhanced the accuracy of traditional predictions. The conclusion of the paper indicates that integrating these technologies into auditing and reporting frameworks has become not only a competitive advantage but a necessity to ensure the reliability and timeliness of financial information.