The Quantum Cosmic Brain (M_QCB) framework proposes information-complexity coupling to gravitational dynamics through a scalar field mechanism. Using Multi-AI Collaborative Orchestration (MACO) methodology coordinating ChatGPT, Claude, and Gemini AI systems, we derive empirically constrained parameters for the QCB scalar sector from two independent scales: (1) galaxy rotation dynamics from SPARC database analysis (N=173 galaxies), and (2) Earth-Moon orbital recession measurements from Lunar Laser Ranging data. Galaxy-scale analysis yields strong correlation (r = 0.851, p < 0.0001) between information complexity and rotation anomalies, establishing effective coupling ratio C_eff ≈ 2.083. Solar System constraints require suppression factor S ≈ 1.418×10⁻¹¹ to match observed 3.8 cm/year lunar recession rate. Cross-scale consistency determines refined parameters: coupling constant γ = 1.0 and characteristic field scale φ_c ≈ 265.6 km. Results demonstrate QCB framework consistency across 10+ orders of magnitude in spatial scale, with cosmological predictions of H_0 = 74.33 km/s/Mpc (resolving Hubble tension) and σ_8 = 0.745. Full methodology, data, and MACO implementation documented for independent replication.