This research investigates the development and implementation of meta-learning systems for computer science skill acquisition through optimized learning pathways utilizing reinforcement learning models. The researcher addresses a critical gap in computer science education, where traditional instructional approaches frequently fail to accommodate diverse learning styles, prior knowledge bases, and cognitive development patterns. The study employs a mixed-methods research design combining quantitative performance metrics with qualitative assessments of learner experiences across multiple cohorts (n=142) of undergraduate and graduate computer science students. Through the implementation of a novel adaptive learning architecture, the researcher demonstrates how reinforcement learning algorithms can effectively model the skill acquisition process, dynamically adjusting content sequencing and difficulty to optimize learning trajectories. Results indicate that students engaging with the meta-learning system exhibited significantly improved performance metrics (27.8% increase in concept mastery, p<0.001) compared to control groups following traditional curriculum structures. Furthermore, the system demonstrated remarkable capability in identifying optimal learning pathways that diverged from expert-designed sequences, particularly benefiting learners with non-traditional backgrounds. Analysis of learning behavior patterns revealed that the system's adaptive mechanisms successfully mitigated common bottlenecks in programming concept acquisition, particularly in abstract data structures and algorithmic complexity domains. The research contributes to both theoretical understanding of meta-learning principles in educational contexts and practical applications for computer science curriculum design, offering implications for intelligent tutoring systems, curriculum development, and lifelong learning frameworks in rapidly evolving technical disciplines.