Maize yield formation is jointly determined by crop-year conditions, water availability, nutrient management, and their interactions, which may include complex nonlinear relationships. This study aimed to identify the principal agronomic and physiological variables associated with maize yield using classical statistical analyses complemented by multilayer perceptron modelling. Field data collected in Debrecen, Hungary, during 2024–2025 included crop year, irrigation, fertilizer treatment, phenological stage, SPAD chlorophyll readings, and grain yield. Pearson correlation and linear regression quantified individual relationships, while four MLP scenarios evaluated combined predictive effects. Fertilizer was positively associated with yield in both years and irrigation regimes, with the strongest relationship under irrigation in 2025 (r = 0.770; R² = 0.594). The SPAD–yield relationship generally strengthened during crop development, reaching its maximum at R3 under irrigation in 2025 (r = 0.916; R² = 0.839). The best-performing MLP scenario included year, fertilizer, and irrigation, confirming their central role in yield prediction and formation.