As for generative AI employed in autonomous data workflows, it is less studied specifically in important undertakings where visions, absence of context, and data drift painstakingly compromise dependability. The existing techniques on text classification such as single model and deep learning are either too shallow or too wasteful of resources. In response, this work proposes a novel lightweight interpretable ensemble framework that incorporates feature extraction through Term Frequency-Inverse Document Frequency (TF-IDF) and employs homogeneous Logistic Regression classifiers integrated via a soft voting scheme. The method transforms sparse textual data into informative TF-IDF vectors, which are then provided to ensembles of bootstrapped logistic regressors. Classification robustness, resource independence, and high interpretability are effectively achieved through the soft-voting ensemble in augmentation of classification trustworthiness and interpretability by operating on the outputs of various models in an efficient manner. Experimental evaluation demonstrates achievement of 98% accuracy, 0.97 F1 score, 0.0088 log loss, and minimal variability in predictions outperforming all conventional approaches and exceeding every metric in performance. The simple yet diverse ensemble achieves powerful results in high-dimensional sparse text classification, providing an efficient approach to scalable low-cost machine learning. The results reveal that combining simplicity with statistical strength and diverse ensembles outperformed intricate designs in defaulted structures.