The intricate networks governing plant-soil interactions constitute the biochemical foundation of terrestrial ecosystem function. Despite their ecological significance, the application of complex network theory to integrate physicochemical, elemental, and microbial parameters remains underexplored. This study employs an interdisciplinary framework combining X-ray fluorescence (XRF) spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, and standard plate count methodology to characterize four distinct land-use soil systems: grassland (S-1), canal-margin (S-2), forest (S-3), and agricultural (S-4). Total microbial counts ranged from 12,910 cfu (S-1) to 20,170 cfu (S-4), following the gradient of anthropogenic disturbance intensity. XRF analysis revealed SiO₂ dominance (66.30–76.50%) with significant inter-site variation in Fe₂O₃, Al₂O₃, and P₂O₅ fractions. FTIR spectra identified distinct functional group signatures, including silicate frameworks (985–1001 cm⁻¹), hydroxyl stretching (3359–3383 cm⁻¹), and aromatic C=C vibrations (1627–1629 cm⁻¹). Correlation analysis and network topology modeling demonstrated that soil moisture (r = 0.91, p < 0.05) and Fe₂O₃ content (r = 0.85, p < 0.05) were the strongest predictors of microbial abundance, while pH and electrical conductivity exhibited moderate positive associations. Network centrality metrics identified total microbial count (TMC) and nutrient uptake as the most influential hub nodes within the plant-soil interaction network. These findings underscore that ecosystem function emerges from non-linear, multi-scale interactions among abiotic matrices, microbial communities, and plant-mediated feedback loops. The study provides a replicable methodological template for integrating spectroscopic and microbiological data into network-based ecological models, offering actionable insights for sustainable land management and soil health monitoring.