<p>Unpredictable operating conditions and old concrete structures cause structural degradation, hydraulic inefficiency, and increased maintenance costs in modern hydroelectric infrastructure. Structural health monitoring models often ignore lifetime economic dynamics, fluid–structure interactions, and material microstructure behavior. Without early hotspot detection, geometric adaptation, and predictive, profit-aligned operations, reactive maintenance is used. To address these difficulties, this work presents a cascading, multi-domain optimization framework that treats a hydroelectric dam as an evolving cyber-physical organism. The Topological Hydro-Structural Fractal Fusion (THS-FF) model combines hydrodynamic pressure topology from computational fluid dynamics with finite-element stress clusters through fractal dimension analysis to create a crack initiation zone hydro-fractal response map. To reduce turbulence and stress, the Adaptive Multi-Scale Morphogenetic Layout Optimizer (AMS-MLO) repeatedly modifies spillway and turbine-hall shape using biological growth criteria. The Probabilistic Material Genomic Mixer (PMGM) stochastically mutates concrete “gene” recipes under environmental stressors and utilizes Bayesian pruning to generate durable, low-carbon material combinations from layouts. These material genome scores control the transformer-based Deep Temporal Vibration Cascade (DTVC), which forecasts deterioration trajectories and useful life using vibration signatures as language tokens. Finally, game-theoretic maintenance strategy competition optimizes net present structural benefit in the Net-Benefit Multi-Objective Economic Decision Engine (NB-MODE). Hydropower plant resilience is improved by this pipeline’s fracture hotspot prediction, hydraulic performance, embodied carbon, maintenance economics, and asset longevity.</p>