We introduce MNPRAS (Morphogenetic Neural Plasticity for Robotic Adaptive Systems), a framework in which a robot’s physical joint topology and neural control architecture are cooptimised as a single unified problem rather than as sequential independent design decisions. The coupling mechanism is the MorphoCode a 64dimensional latent vector that encodes the body graph using a contrastivelytrained Graph Isomorphism Network, with architecture information entering implicitly through the performance signal. When the robot’s physical configuration changes, the updated MorphoCode retrieves a warmstart from a prebuilt corpus, reducing postreconfiguration adaptation time by up to 3.8× compared to coldstarting. Physical adaptation uses a modular platform with electromagneticallyactuated coupling interfaces completing engagement in under 420 ms. Across 14 simulated environments and 3 realworld task deployments, MNPRAS achieves 63.5% higher mean episode reward than the strongest fixedmorphology baseline and reaches competent performance 2.6× faster than that baseline. Critically, trained agents reconfigure their bodies an average of 80 ms before environmental transitions rather than in response to them a predictive adaptation behaviour that emerges from reward alone, without explicit anticipation supervision.