The use of neural networks in the hardware processing for Artificial Intelligence (AI) has gained significant traction, particularly for solving complex, non-linear, and chaotic problems. A primary objective of this research is to explore the optimal environment for neuromorphic chips, enabling advancements such as increased neural connections, self-healing capabilities, enhanced memory capacity, and energy efficiency. This study focuses on the impact of two critical environmental factors—humidity and temperature cycles—on the dynamic behaviour of Ag-PVP nanowire networks. Through conductance measurements, this research seeks to demonstrate both potentiation and relaxation correlations with external conditions in these networks. The experiments were conducted in a controlled laboratory environment at the Polytechnic University of Turin, utilizing the Keithley 4200-SCS Semiconductor Characterization System for data collection and the Precision Humidity Control System (HCS-2M) for environmental regulation.