Accurate modeling of biochemical oxygen demand over 20 days (BOD₍₂₀₎) remains essential for understanding the biodegradation dynamics of organic matter in urban aquatic systems. In this study, we apply artificial intelligence (AI) techniques to estimate kinetic parameters of the BOD₍₂₀₎ process in lakes of Szczecin, Poland—ecosystems subjected to high anthropopressure and climatic variability. Based on daily incubation tests performed in dark conditions at 20°C, we reconstructed multi-phase BOD₍₂₀₎ curves and extracted key kinetic descriptors: the delay phase (t₀), exponential growth (t₁), monomolecular reaction (t₂), oxygen consumption markers (y₁, y₂), ultimate and residual BOD (L₀, L₁), growth and degradation rate constants (Kᵥ, K₁), and endogenous respiration slope (Re). The classical Fujimoto method and continuity conditions were used to derive K₁ and Kᵥ analytically. The dataset integrates daily-resolved BOD₍₂₀₎ kinetic indicators (t₀, t₁, t₂, L₀, L₁, y₁, y₂, K₁, Kᵥ, Re) with a broad spectrum of environmental variables, including over 50 physicochemical water quality metrics (e.g., DO, pH, COD, nutrient loads, metal concentrations), meteorological conditions (temperature, wind, solar radiation, precipitation), and seasonal or site-specific anthropopressure indicators. Various AI models—ranging from ensemble learning to neural networks and hybrid approaches—have been explored to capture complex relationships governing oxygen demand dynamics. Preliminary results confirm the high potential of AI-based methods to enhance predictive accuracy, reveal latent patterns, and support robust water quality assessment under rapidly changing environmental conditions.