Sentiment analysis in Tamil-Code Mixed data is challenging mainly due to its linguistic challenges. This paper analyzes several methodologies like deep learning (DL), machine learning (ML) and transformer-driven approaches and at the same time proposing a BiLSTM-driven approach for evaluating the sentiment of a Code-Mixed Tamil sentence. This research compares two approaches to analyze sentiment polarity in Code-Mixed Tamil sentence, BiLSTM and KNN, out of which the BiLSTM algorithm came out as the better algorithm for sentiment classification for Code-Mixed Tamil Text. The methodology achieves an F1 score of 0.435563, an accuracy of 0.746383, a precision of 0.332918, a recall of 0.629717, and a loss of 0.01 for the BiLSTM algorithm, and for the KNN algorithm, the methodology achieves an F1 score of 0.235714, a recall of 0.155660, a precision of 0.485294, and an accuracy of 0.828731. This study highlights several methods such from TF-IDF-based models to transformer architectures, highlighting the challenges faced during sentiment analysis in Code-Mixed Tamil sentences.