In modern processor development, extensive simulation is required before manufacturing to ensure that Central Processing Unit (CPU) designs function correctly and efficiently. This pre-silicon validation process involves running a wide range of software workloads on architectural models to identify potential issues early in the design cycle. Improving pre-silicon simulation time is critical for accelerating CPU development and reducing time-to-market for high-quality processors. This study addresses the computational challenges of validating full-system simulations by leveraging unsupervised machine learning to optimize test case selection. By identifying patterns in executed instructions, the approach reduces the need for exhaustive simulations while maintaining rigorous validation standards. Notably, the optimized subset of test cases reduced simulation time by a factor of 10 and captured 97.5% of the maximum instruction entropy, ensuring nearly the same diversity in instruction coverage as the full workload set. The combination of Principal Component Analysis (PCA) and clustering algorithms effectively distinguished compute-bound and memory-bound workloads without requiring prior knowledge of the code. Statistical Model Checking with entropy-based analysis confirmed the effectiveness of this subset. This methodology significantly reduces validation effort, expedites CPU design cycles, and improves hardware efficiency. The findings highlight the potential of machine learning-driven validation strategies to enhance pre-silicon testing, enabling faster innovation and more robust processor architectures.