The fragmented landscape of existing computational tools often hinders the seamless integration of large-scale structure prediction with rigorous stability validation. To address this, we present GEWUM (General Exploration Workflow for the Utopia of Materials), an open-source platform that integrates the Selective Random Structure Search (SRSS) strategy with universal Machine Learning Interatomic Potentials (uMLIPs) to automate and accelerate materials discovery. With native support for SLURM-based HPC clusters, GEWUM unifies the entire workflow from structure generation and diversity-preserving selection to thermodynamic/dynamic stability assessments and property calculations. The platform further incorporates built-in visualization tools, including Sankey diagrams for space-group evolution, violin plots for energy distributions, and t-SNE/UMAP embeddings for structural diversity, enabling intuitive interpretation of screening results. We demonstrate GEWUM through three case studies: low-energy polymorph prediction in Al-Sc-N, identification of a P-62c phase of U3Si5, and high-pressure structure prediction of ThH10 at 150 GPa. Benchmark tests confirm reasonable agreement in thermophysical property predictions.