Efficient energy usage is vital for extending Wireless Sensor Networks (WSNs) lifespan. While Improved LowEnergy Adaptive Clustering Hierarchy (ILEACH) excels in energy-efficient data aggregation, challenges like premature cluster head $({\text{CH}})$ failure remain. Genetic Algorithm (GA) optimizes parameters, including energy, in WSNs. We propose a novel hybrid ILEACH-GA algorithm for data aggregation. ILEACH forms clusters, GA evaluates fitness, selecting optimal clusters for aggregation. GA mitigates ILEACH's premature CH failure. ILEACH-GA surpasses LEACH, ILEACH, and GA-LEACH, with significantly higher throughput $(10.0\%$, $47.4{{\% }},21.9{{\% }}$ respectively), retaining higher residual energy (0.0805) and alive nodes $({25.5{{\% }}})$. This innovation boosts sustainable WSN data aggregation, overcoming limitations, and enhancing performance. This innovation elevates sustainable WSN data aggregation, surmounting limitations, and augmenting performance, applicable in waste and crop management systems.