K. Vijay Kumar, Chittimalla Akhila, Banavath Shailaja, Polepaka Ramesh, Chiprishetti Nithish 03 06 The expansion of digital platforms has resulted in a substantial increase in employeegenerated feedback making the assessment of workforce satisfaction a critical research focus Conventional methods such as manual surveys and basic statistical analyses are often inefficient and inadequate for extracting meaningful insights from complex textual data Recent developments in Natural Language Processing NLP and Machine Learning ML have enabled automated approaches however many existing solutions face challenges with unstructured text imbalanced datasets and multilabel prediction tasks This study focuses on improving the prediction of key workforce satisfaction dimensions including worklife balance skill enhancement compensation and benefits job stability career advancement and overall job satisfaction based on employee reviews Traditional techniques are limited in scalability and often deliver inconsistent results highlighting the necessity for a more advanced and reliable analytical framework To address these limitations the proposed approach combines NLPbased preprocessing transformerdriven feature extraction using Google PaLM Pathways Language Model and the Synthetic Minority Oversampling Technique SMOTE It evaluates multiple machine learning models Quadratic Discriminant Analysis QDA Linear Discriminant Analysis LDA and HistogramBased Gradient Boosting HGB and compares them with a newly introduced TransformerGuided Adaptive Model TGAM Experimental findings indicate that conventional models achieve accuracy levels between approximately 51 and 56 whereas the TGAM model attains a perfect accuracy of 100 across all evaluated categories These results demonstrate the robustness and effectiveness of the proposed system in analyzing complex employee feedback data supported by comprehensive evaluation metrics and visualization methods for enhanced interpretability Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026