The intersection between technology and education has taken on unprecedented relevance, driven by the promise of transforming teaching and learning through advanced digital tools. This study proposes a comprehensive exploration of how cloud computing and artificial intelligence converge to impact education, focusing on accessibility, efficiency, and quality of learning. A mixed-research design identified a 25% improvement in the personalization of educational content thanks to AI and a 60% increase in simultaneous user capacity through cloud computing. Additionally, a significant reduction in administrative errors and improvements in scalability were observed without sacrificing quality. The results demonstrate that these technologies not only improve efficiency and accessibility in education but also enrich the learning experience. By comparing these findings with previous research, this study highlights the synergistic value of these technologies and positions itself as a critical resource to guide future developments and improvements in the education sector in a digitally advanced world.

Effective student retention in higher education represents a critical challenge to institutional stability and educational quality. This study addresses this challenge by integrating machine learning and artificial intelligence techniques in the context of sustainability education. To achieve this, data are collected from a representative cohort of students undergoing extensive cleaning and pre-processing. Additionally, a pre-trained neural network model is implemented, adjusting key parameters. The model evaluation was based on relevant metrics and error analysis, demonstrating that integrating machine learning and artificial intelligence allows early identification of at-risk students and the provision of personalized interventions. This study addresses contemporary student retention challenges in three critical areas: the transition to online education, student mental health and well-being, and equity and diversity in access to higher education. These challenges are addressed through specific strategies based on data analysis and machine learning, thus contributing to overcoming them in the context of higher education. Additionally, this study prioritizes ethical concerns when applying these technologies, ensuring integrity and equity in decision-making related to student retention. Together, this work presents an innovative approach that uses machine learning and artificial intelligence to improve student retention within the framework of educational sustainability, highlighting its transformative potential in higher education.