This article synthesizes theoretical perspectives and applied insights drawn from a curated set of contemporary and foundational works to present an integrative, publication-ready examination of graphics processing units (GPUs) as central engines of modern high-performance computing (HPC), machine learning, and real-time multimedia systems. We develop a coherent narrative that traces GPU evolution and architectural principles, explores GPU programming models and their implications for large-scale data mining and accelerated computing, interrogates energy, power, and thermal management across device-to-application layers, and details validation and manufacturing strategies for acoustic and thermal integrity. Methodologically, the work adopts a cross-disciplinary descriptive synthesis grounded in primary references, combining architectural analysis, systems-level power and thermal modeling concepts, and process- and design-oriented validation approaches. Results are presented as a rich descriptive analysis that elucidates (1) how architectural choices have shaped parallel programming paradigms and application performance, (2) the complex trade-offs between performance, energy consumption, and thermal constraints in GPU-centric systems, (3) mechanisms for integrated CPU–GPU power management in constrained environments such as mobile gaming, and (4) scalable acoustic and thermal validation strategies needed in modern GPU manufacturing. We interpret these findings to argue for a layered, co-designed approach that couples architectural innovations (including 3-D integration and GPU-in-memory concepts) with machine-learning-aided power/thermal management and scalable manufacturing validation. The discussion highlights limitations of current approaches—particularly the challenges in generalizing thermal models across heterogeneous stacks and the nascent state of AI-driven thermal control for GPUs—and proposes a future research agenda that emphasizes co-design, domain-specific cooling techniques, hardware/software power coordination, and standardized validation pipelines. This integrative treatment aims to inform researchers, system designers, and manufacturing engineers seeking to align GPU architecture, system-level energy efficiency, and robust validation practices in the era of AI-scale computing.