Sentiment analysis, a subfield of natural language processing (NLP) has witnessed significant advancements in the analysis of usergenerated contents across diverse languages. However, its application to low-resource languages remains a challenge. This research addresses this gap by conducting a comprehensive sentiment analysis experiment in the context of the Mizo language, a low-resource language predominantly spoken in the Indian state of Mizoram and neighboring regions. Our study encompasses the evaluation of various machine learning models including Support Vector Machine (SVM), Decision Tree, Random Forest, K-Nearest Neighbor (K-NN), Logistic Regression and transfer learning using XLM-RoBERTa. The findings reveal the suitability of SVM as a robust performer in Mizo sentiment analysis demonstrating the highest F1 Score and Accuracy among the models tested. XLM-RoBERTa, a transfer learning model exhibits competitive performance highlighting the potential of leveraging pre-trained multilingual models in low-resource language sentiment analysis tasks. This research advances our understanding of sentiment analysis in lowresource languages and serves as a stepping stone for future investigations in this domain.