Energy preservation remains a crucial challenge in the Heterogeneous Wireless Sensor Networks (HWSNs) deployment. The uneven distribution of primary energy leads to precocious node depletion and network segmentation. Five intelligent routing and clustering optimization strategies tailored for dynamic and heterogeneous environments are presented and comprehensively compared in this paper: the proposed Binary-Chromosome Genetic Algorithm (BC-GA), Grey Wolf Optimization (GWO), Deep Q-Learning (DQL), Distributed Energy-Efficient Clustering (DEEC) and Power-Efficient Gathering in Sensor Information Systems (PEGASIS). The simulations are performed on a 100×100 m² field with 50 randomly deployed nodes having different initial energy levels (0.507- 0.986 J) communicating with a Base Station. Empirical results obtained from five trial Monte Carlo simulations expose excellent performance trade-offs. BC-GA achieves an outstanding Last Node Dead (LND) of 5339.60 ± 157.57 rounds and maximum throughput of 166,219.40 ± 2534.72 packets. Among the classical baselines, PEGASIS achieves an LND of 3095.20 ± 212.68 rounds and throughput of 120,742.00. On the other hand, GWO and DQL have accelerated depletion phases with FND 432.40 ± 62.15 and 568.80 ± 48.30 rounds, respectively, due to extreme pressure on the bottleneck node near the Base Station. The End-to-End (E2E) delay provided by DEEC is the minimum (11.49 ± 0.36 ms) among all methods and the Energy-Delay Product (EDP) is the best (6.09 ± 0.38) which makes DEEC very suitable for latency-critical environments. Those results prove that the evolutionary cluster-head structuring provided top-level load balance and an extension of lifetime for intensive data monitoring.