The process of understanding the underlying subjective information and opinion in a given sentence is sentiment analysis. Accurate Sentiment analysis is possible with the use of Deep-Learning techniques. We have proposed three Deep-Learning based fine-tuned models for the three-class sentiment analysis task. It is complemented with an end-to-end pipeline that includes classical approaches like stemming, lemmatization in the data preprocessing, parameter tuning, and so forth for Marathi texts analysis. The proposed models are based on ANN, IndicBERT, and BiLSTM. The “L3CubeMahaSent'’ dataset was used for both training and testing of the proposed models. The IndicBERT based model achieved a superior F1-score of 85%, Precision of 85% and a recall score of 86% on the dataset. The overall weighted accuracy of this model was 81%. Additionally, all the proposed models achieved higher F1-scores and accuracy in predicting the negative sentiments compared to neutral and positive ones even in the class-balanced dataset.