A. Hareesha, Prasanna Laxmi Medichelmi, Mudireddy Sandeep Reddy, Rohini Moola 03 04 The rapid growth of digital platforms has led to the generation of large volumes of employee feedback data making workforce satisfaction analysis an important area of study Traditionally organizations relied on manual surveys and basic statistical methods to evaluate employee satisfaction which were often timeconsuming and limited in capturing complex textual insights With advancements in Natural Language Processing NLP and Machine Learning ML automated analysis has become feasible However existing approaches struggle with unstructured text class imbalance and multidimensional prediction tasks The primary problem addressed in this study is the accurate prediction of workforce satisfaction factors such as worklife balance skill development salary and benefits job security career growth and overall satisfaction from textual employee reviews Traditional systems fail to process largescale data efficiently and lack consistency in predictive performance This creates the need for an intelligent framework capable of handling complex textual patterns and multilabel classification To overcome these challenges the proposed system integrates NLP preprocessing transformerbased feature extraction using Google PaLM Pathways Language Model PaLM and SMOTE Synthetic Minority Oversampling Technique Multiple ML models including Quadratic Discriminant Analysis QDA Linear Discriminant Analysis LDA and HistogramBased Gradient Boosting HGB are implemented and compared with the proposed TransformerGuided Adaptive Model TGAM The results show that traditional models achieve moderate accuracy ranging from approximately 51 to 56 while the proposed TGAM model achieves 10000 accuracy across all target columns including worklife balance skill development salary and benefits job security career growth and work satisfaction This significant improvement highlights the effectiveness of the proposed approach in handling complex workforce data The system also includes evaluation metrics and visualization techniques for better interpretability Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026