The rise of Generative AI has renewed interest in Deep Learning across academia and industry, attracting smaller research groups and non-IT companies eager to leverage Machine Learning (ML). However, high infrastructure costs often make AI adoption impractical. Democratizing access to High-Performance Computing (HPC) is key to overcoming this barrier, enabling broader ML adoption while reducing e-waste by integrating existing resources. DARE-ML (Democratized Accessible Resource Environment for Machine Learning) offers a framework to optimize resource allocation, lower energy use, and improve ML accessibility. By profiling models interactively in a heterogeneous, limited-GPU environment, DARE-ML collects key metadata—like training time and memory needs—before scheduling jobs. At its core, DARE-ML incorporates an efficient interactive profiling mechanism powered by ESN (Echo State Networks), enabling streamlined and resource-aware execution of deep learning tasks. Experiments in real and simulated settings show DARE-ML improves ML job scheduling, reducing energy use up to 80-fold and cutting both average job completion time and waiting time per user by 15%.