Scaled implementation of the DevOps strategy became to be a significant part of present listing, and, constructive information technology in general, since it leads to a smooth fusion of the development and the operations team in achieving software projects, and doing so within a high level of accuracy. As its disadvantages, traditional methods were characterized by slow delivery of software, a large share of errors, and incapability of quickly reacting to the changes. In order to deal with those challenges, a number of machine learning techniques have been used to inject a predictive layer into DevOps practices. The study was based on the HELENA dataset which is a high quality global dataset that was collected after many years of study across the different countries in the world. In this research article, Adaboost model has been suggested underlining its use as standard in organizing the development of software systems that offers high accuracy outcomes of up to 98%. This, furthermore, demonstrates the criticality of the role it plays in software development improvement in terms of performance, development team preparedness, and shortened software life cycle achieved, by saving time, efforts, and development expenses.