In the constantly evolving world of cloud computing, appropriate resource allocation is essential for both keeping costs down and ensuring an ongoing flow of apps and services. Because of its adaptability to specific tasks and human behavior, machine learning (ML) is a desirable choice for fulfilling those needs. This study Efficient cloud resource allocation is critical for optimizing performance and cost in cloud computing environments. In order to improve the precision of resource allocation, this study investigates the use of Long Short-Term Memory (LSTM). The LSTM model achieved 97% accuracy, 97.5% precision, 98% recall, and a 97.8% F1-score (F1-score: harmonic mean of precision and recall), according to experimental data. The confusion matrix demonstrates strong classification performance across several resource classes, while the accuracy and loss curves verify steady learning with minimal overfitting. The suggested LSTM model performs better than more conventional ML (machine learning) models like Gradient Boosting (GB) and Logistic Regression (LR), according to a comparative study. These findings underscore the LSTM (Long Short-Term Memory) model’s robustness and suitability for dynamic cloud environments, enabling more accurate forecasting and efficient resource management.

The continuous integration (CI) and continuous delivery/deployment (CD) methods are key tools in the field of modern software development, and they assist in the rapid, reliable and quality delivery of software. These DevOps methods are automated, and the code development, testing, and deployment processes are streamlined, which reduces the risk of integration, enhances productivity, and minimizes human labor. To implement CI/CD, Java cloud applications can utilize cloud-native services such as AWS Code Pipeline, Azure DevOps, and Google Cloud Build, as well as tools like Jenkins, GitLab CI/CD, GitHub Actions, CircleCI, Travis CI, and Bamboo. Basic concepts of CI/CD include automation, regular integration, testing, intensive testing, constant feedback, and process improvement. Some of the major pipeline phases include deployment, monitoring, testing, artefact management, build automation, and source code management. Despite clear benefits, challenges remain, including infrastructure complexity, dependency management, test reliability, and cultural barriers, particularly in large-scale or enterprise Java projects. This work provides a thorough analysis of CI/CD procedures and resources, including frameworks, best practices, and challenges for Java cloud applications. It highlights strategies to optimize adoption, improve software quality, and accelerate delivery cycles.