Stemming is an important preprocessing step in Natural Language Processing because it helps handle the rich morphological structure of the Bangla language which increases vocabulary size and affects model performance. This study evaluates different Bangla stemming techniques for text classification tasks using both traditional and deep learning classifiers including Nave Bayes, Support Vector Machines, Random Forest, LSTM, CNN-BiLSTM and BanglaBERT. Experimentation on a Bangla news corpus consisting of 67,564 instances belonging to seven classes demonstrated that stemming reduces vocabulary size by 41% to 52%, enhances classification accuracy and reduces processing time. Hybrid stemmers invariably produced better results than either rules-based methods or statistical approaches. Additionally, the paper presents an exhaustive analysis of errors which include issues like over stemming, under stemming, morphologically ambiguous forms, compound words and loanwords among others. The results emphasize the importance of the role of stemming towards improving the Bangla NLP and provides a good foundation for future research.