Pagalla Bhavani Shankar1 | Dr M Babu Reddy2 | Surla Kusa Raju3 | Shaik Moulasa4 | Muppalla Sunil5 | Nagula Hema Satya Kumar6 | Dr K Abida Begum 7 03 04 Deep learning models have made great progress in many areas but they are limited by their fixed structure and singlestep optimization process which makes it hard for them to adjust to changing data over time This paper presents a Dynamic Nested Optimization Framework DNOF based on the idea of nested learning where learning is organized as a layered system of connected optimization steps that work across different time frames and contexts Unlike standard methods that use just one type of gradient update this framework uses multiple layers of optimization This lets the system learn model settings optimization techniques and memory structures at the same time The layered design helps the system understand both shortterm and longterm patterns making the learning process more stable and the model more accurate The model was tested on a structured dataset and performed very well achieving an accuracy of 98986 These results show that the model learns efficiently and remains stable The findings show that nested optimization improves prediction accuracy and makes training more reliable This research helps move machine learning forward by connecting traditional deep learning methods with adaptable smart systems It provides a flexible base for ongoing learning and future AI models Copyright (c) Journal of Engineering Technology and Sciences. All rights reserved 2026