Performance optimization in NumPy hinges on selecting appropriate data types that align with hardware capabilities. Utilizing 8-bit integers instead of 64-bit can reduce cache misses and enhance CPU register use. Employing np.float32 over np.float64 minimizes memory usage while maintaining sufficient precision, critical for large-scale data operations and machine learning tasks.