As we enter a golden age of data-driven cosmology, multi-wavelength surveys are set to revolutionize our understanding of the universe. Ongoing and upcoming observations — ranging from Sunyaev-Zel'dovich (SZ) effects to X-ray emissions, fast radio bursts (FRBs), and absorption line studies — promise unprecedented insights into the astrophysical processes driving galaxy formation and evolution, the impact of baryonic physics on cosmological observations, and the fundamental parameters of our universe. In this talk, I will explore key insights derived from CAMELS (Cosmology and Astrophysics with MachineE Learning Simulations) simulations and outline a strategic roadmap for optimizing scientific returns from these surveys. By integrating simulation data with advanced machine learning techniques, we can enhance our interpretations and guide future research directions in precision cosmology and galaxy astrophysics.