For finding patterns in data, machine learning models are being trained. Gender relations psychology looks for social norms like inter dimensionality, beliefs, social experience and self-perception, and self-respect. Training on gender based text NLP models unknowingly become acquainted with unusual patterns. In this paper, we represent gender recognition by using Bengali conventional full names. We present a review and interpretation of gender classification based on individual names in this correspondence. These days, NLP has demonstrated excellent execution in identifying human gender. In the field of knowledge, gender classification is a demonstrative binary classification phenomenon. We've used a total of seven algorithms in this research. We were added to the dataset with details regarding which features are currently used for prediction along with that it determines how these features are affected by data preprocessing model initialization and architecture selection. Our research compares those classifiers, examines the impact of pretraining moreover, assesses the robustness of the alignment preprocessing through the confusion matrix.. The proposed Neural Network outperforms most approaches and is much more reliable than other models. This model has the best weighted precision of all the models, with such a 73.04 % accuracy score.