Replacing chemical fertilizers with biogas slurry microbial fertilizer (BSMF) represents an important strategy for sustainable agriculture in arid regions, yet optimal substitution strategies and ecological response dynamics remain poorly understood. A three-year field experiment monitored soil physicochemical properties, metagenomic profiles, metabolomic signatures, and tomato performance across a 0%-100% BSMF substitution gradient in a Ningxia greenhouse system. Multi-omics integration identified differential sensitivity patterns across biological layers. BSMF substitution induced non-linear ecological reorganization following a tiered sensitivity pattern. Microbial functional potential adapted first (10%-30% substitution), with carbon cycling genes like xynA increasing 57.2%. Community restructuring peaked at 20%-30% substitution (117 genera changed during S20K-S30K transition), while metabolome reprogramming occurred at higher thresholds (above 40%, with 95 metabolites altered between S40K-S50K). Intermediate substitution levels enhanced yield stability, reducing coefficients of variation from above 23% (conventional) to approximately 8% (S10K, S50K). The 20-30% range optimized productivity while maintaining stability. Fruit quality improvements were time-dependent; vitamin C doubled from 836.7 mg/kg to 1816.7 mg/kg, while nitrate content decreased by 87%. Multi-omics integration revealed microbial carbon cycling potential significantly correlated with crop yield (r = 0.495, p = 0.043). However, substitution above 50% elevated salinity risk. Soil ecosystems responded to organic fertilizer substitution through predictable, threshold-driven adaptations rather than linear responses. The tiered sensitivity pattern, with functional plasticity preceding community restructuring, followed by metabolic reprogramming, provided a mechanistic framework for precision management. The 20%-30% substitution range represented an optimal ecological window balancing productivity and stability while avoiding salinity constraints, transforming BSMF application from empirical adjustment to predictive management for resilient arid agroecosystems.