Soil constraints significantly impact crop productivity, yet their direct relationships to yield remain unclear. This limits the development of targeted soil management strategies for improving agricultural output. This study aims to clarify the influence of key soil constraints on crop productivity by examining soil chemical indicators across distinct productivity zones in Queensland. Soil samples were collected from 21 farms across three productivity zones (consistently low, inconsistent, and consistently high) and five soil layers (D1–D5, 0–10, 10–30, 30–60, 60–90, and 90–120 cm). We utilized the Constraint ID tool alongside mixed-effects models, principal component analysis (PCA), and machine learning to evaluate indicators including nitrate (NO3−), electrical conductivity of the saturated soil extract (ECe), pH, chloride (Cl), exchangeable sodium percentage (ESP), and exchangeable cations (Ca, K, and Mg). This integrated approach—among the first in Queensland—enabled depth-specific and spatially explicit analysis of soil constraint impacts. ECe, pH, Cl, and ESP are critical factors influencing soil fertility, particularly in subsoil layers (D3–D5). Low-yielding zones (Zone L) exhibited high pH (up to 8.41), Cl (up to 151 mg/kg), ESP (up to 8.64), and ECe (exceeding 4 dS/m), indicating salinity, alkalinity, and sodicity issues. These subsoil constraints are difficult to remediate, highlighting the need for surface-level strategies that support whole-profile soil health. This study underscores the necessity of site-specific, surface-focused interventions that address constraints across the entire soil profile. The findings offer actionable insights for tailoring soil management and support regional decision-making to optimize crop yields in Queensland's agricultural systems.