Industry professionals predict that the use of artificial intelligence and machine learning will significantly impact people's lives in the near future. They believe that it will allow individuals and organizations to perform more efficiently by improving their processes and making them more effective at their jobs. Machine learning could help improve employees' work-life balance by allowing them to perform more efficiently at their jobs. It could also increase the general quality of their lives. What would be the outcome of an experiment involving machine learning to find ways to improve the balance between work and life? The study aimed to analyze individuals' subjective feelings about work-life balance. Through a machine learning method, the researchers could identify the characteristics influencing the balance between work and life. They then used a forecasting model to analyze the data and determine how much women in the IT industry observe a work-life balance. The study was conducted on 150 female IT professionals. The researchers used a questionnaire to gather information about the participants. They then used various deep-learning models to analyze the data. The researchers analyzed the models' performance by calculating the mean absolute and square errors. They found that the models known as the Multilayer Perceptron and the Long Short-term Memory performed well in terms of their accuracy when forecasting future business conditions. Comparing the two models revealed that the Short-Term Long Memory performed better than the MLP in terms of accuracy. This finding could be beneficial for studies related to the behavior of organizations. © 2022 ACM.