Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3938 2019 2019 University of Information Technology and Communications (UoITC), Baghdad, Iraq Oday Oday University of Information Technology and Communications (UoITC), Baghdad, Iraq Bushra Al Al-Saadi Department of Computer Science, University of Al Maarif, Al-Anbar, 31001, Iraq Dheyauldeen Ahmed Farhan Ministry of Education, Wasit Education Directorate, Iraq; Computer Department, College of Education for Pure Sciences, Wasit University, 52001 Al-Kut, Wasit, Iraq Oday Ali Hassen Deep studying architectures face fundamental demanding situations in balancing overall performance optimization, computational scalability, and operational interpretability. Current strategies show off an essential fragmentation: neural architecture search (NAS) techniques perform independently of interpretability requirements, while scalability answers remain detached from structure optimization pipelines. This disconnect hinders the improvement of a unified workflow from architecture layout to interpretable deployment. We endorse DeepOptiFrame, a TensorFlowKeras-primarily based Python framework that combines three middle capabilities: (1) superior optimization algorithms (BOHB, Hyperband) with useful resource-restrained multi-objective search, (2) distributed training acceleration across GPUGPU clusters via Horovod integration and blended-precision strategies, and (3) GPU-increased interpretability gear (SHAP, LIME) incorporated without delay into the education pipeline. Our framework demonstrates large experimental improvements: a 15-20% accuracy growth at the CIFAR-a hundred and ImageNet benchmarks compared to today's baselines, a 65% education speedup whilst scaled to eight GPUs with close to-linear performance, and a 30% development in interpretability reliability, as measured via the Mean Confidence Decrease metric. This implementation additionally reduces reminiscence intake via forty% throughout gradient checkpoints even as keeping numerical balance. These advances establish a new paradigm for coherent deep learning development, simultaneously improving overall performance, scalability, and transparency inside unified workflow surroundings. 2026 2026 114 125 10.54216/JISIoT.180108 https://www.americaspg.com/articleinfo/18/show/3938