In this study, we evaluate predictive modelling techniques within project management, employing diverse architectures such as the LSTM, CNN, CNN-LSTM, GRU, MLP, and RNN models. The primary focus is on assessing the precision and consistency of predictions for crucial project parameters, including completion time, required personnel, and estimated costs. Our analysis utilises a comprehensive dataset that encapsulates the complexities inherent in real-world projects, providing a robust basis for evaluating model performance. The findings, presented through detailed tables and comparative charts, underscore the collective success of the models. The LSTM model stands out for its exceptional performance in consistently predicting completion time, personnel requirements, and estimated costs. Quantitative evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), corroborate the efficacy of the models. This study offers insights into the success observed, reflecting the potential for further refinement and continuous exploration to enhance the accuracy of predictive models in the ever-evolving landscape of project management.

There are many methodologies that we can follow in software projects, including the traditional methods, at the forefront of which it was effective and with a good result, but with the constant increase in requirements resulting from the continuous and rapid development in science and technology and digital transformation; Traditional, highly structured project management methods lack flexibility, awareness of change, adaptability to it, and independence; It led to developing this methodology into an Agile methodology in the form it is currently known, which contributed to developing the team’s skills and knowledge. Our focus in this paper is a study on a data set affiliated with the Agile methodology by measuring effort using one of the well-known methods, monitoring the actual time and comparing it with the expected time to deliver a specific work, and also identifying the difficulties they face while working in different environments to build a comprehensive picture of the benefits and advantages of the Agile methodology.