We present ENAS, a hardware-aware Neural Architecture Search (NAS) framework that combines a static feasibility check, a cell-based search space supporting standard, depthwise-separable, and bottleneck blocks with optional skip connections, and a three-stage hybrid search strategy (random → top-K → mutation) with persistent cross-run caching. Unlike many existing NAS frameworks that rely on GPU acceleration, ENAS is designed to operate efficiently without requiring GPUs, making it suitable for resource-constrained development environments. We evaluate ENAS on two TinyML benchmarks, Visual Wake Words and Melanoma Cancer, across eight microcontrollers with memory footprints ranging from 20 KB to 1 MB SRAM and nine input image resolutions. Our experimental results show that ENAS achieves mean search-time speedups of 2.41× and 1.70× on the Visual Wake Words and Melanoma Cancer datasets, respectively, while maintaining competitive test accuracy compared with the recent NanoNAS framework. A measured resource analysis further shows that ENAS-selected models use substantially lower peak activation RAM, the binding constraint for microcontroller deployment at matched accuracy. Additionally, ENAS achieves 79.4% test accuracy on an STM32H743-based microcontroller, outperforming the greedy CPU-only baseline by 2.6 percentage points. We release the ENAS framework as open-source at: https://github.com/EdgeIntelligenceLab/ENAS