AI-assisted electronic nose systems often emphasize sensitivity-driven datasets, overlooking the comprehensive analysis of gaseous chemical attributes critical for precise gas identification. Conventional fabrication methods generate inconsistent datasets and focus primarily on improving classification accuracy through deep learning, neglecting the fundamental role of sensor material design. This study addresses these challenges by developing a highly reliable sensor platform to standardize gas sensing for deep learning applications. Specifically, 1D SnO₂ nanonetworks functionalized with Au and Pd nanocatalysts are fabricated via a systematic deposition process, enhancing gas diffusion and reaction kinetics. Stability improvements through controlled aging process reduce the coefficient of variation to below 5% across seven target gases: acetone, hydrogen, ethanol, carbon monoxide, propane, isoprene, and toluene. The platform exhibits exceptional deep learning performance, achieving over 99.5% classification accuracy using a residual network model, even in high-humidity environments (up to 80% relative humidity) and at parts-per-trillion detection limits. This study highlights the synergy between nanostructure engineering and AI, establishing a robust framework for next-generation bioinspired electronic nose systems with enhanced reliability and analytical capability.