The purpose of this study is to develop strategies for enhancing higher and postgraduate education in Kazakhstan, with a focus on improving graduate readiness for professional roles and building predictive models to analyze and assess doctoral graduation rates in technical fields for 2023–2025. This research emphasizes a flexible framework that integrates universal competencies into engineering programs, adaptable to various institutional and national contexts. Active learning methods, including project-based tasks, problem-solving exercises, internships, and lab work, are employed to cultivate practical skills aligned with real-world engineering demands. The study also highlights the need for comprehensive assessment systems that extend beyond theoretical testing, incorporating continuous feedback and curriculum adjustments to align with labor market needs and scientific developments. Results reveal key areas for higher education reform, as outlined in Kazakhstan’s State Program for Education and Science Development (2020–2025). These include implementing a competency-based approach, optimizing interdisciplinary and practice-oriented teaching methods, and enhancing the global competitiveness of Kazakhstani universities. Additionally, through time-series analysis of doctoral graduation data, a trend model was created, showing a steady increase in graduates, tested for anomalies and trend adequacy, making it reliable for forecasting graduate numbers. The scientific significance of this work lies in its contributions to models that improve graduate training, fostering high-quality education to enhance Kazakhstani universities’ global competitiveness. By integrating AI-driven strategies and emerging technologies, the research provides an adaptive educational model to equip students with essential professional and universal competencies, essential for navigating the evolving labor market and supporting sustainable development.