This study explores the application of sentiment analysis to election data using various machine learning and ensemble algorithms. Sentiment analysis or opinion mining, leverages natural language processing (NLP) to extract and analyze subjective information from textual data, providing valuable insights into public opinion. Our research focuses on social media platforms, where vast amounts of user-generated content reflect voter sentiments towards candidates, policies and parties. The dataset, collected during an election campaign period, undergoes extensive pre-processing, including cleaning, tokenization, normalization, and feature extraction using techniques known as Term Frequency-Inverse Document Frequency (TF-IDF). We employ multiple machine learning algorithms - Naive Bayes, Support Vector Machines, Logistic Regression, Random Forest, Gradient Boosting Machines and Neural Networks—to predict sentiment. Additionally, we implement ensemble methods like voting classifiers, stacking, bagging, and boosting to enhance prediction accuracy. Performance metrics, including accuracy, precision, recall, F1-score and Receiver Operating Characteristic Curve-Area Under Curve (ROC-AUC), are used to evaluate and compare models. The results reveal significant insights into sentiment trends over time, highlighting correlations between public sentiment and key election events. The study underscores the potential of sentiment analysis as a predictive tool for election outcomes, despite challenges such as data quality and natural language complexity. Our findings contribute to the understanding of voter behavior and offer practical implications for political campaign strategies BJP (Bharatiya Janata Party) and INC (Indian National Congress). This comprehensive analysis showcases the integration of machine learning and ensemble approaches in enhancing the accuracy and reliability of sentiment analysis in the political domain.