Aspect-Based Sentiment Analysis (ABSA) has emerged as a vital domain within Natural Language Processing (NLP) that extracts and classifies sentiment for specific aspects within text. With the surge in user-generated content, ABSA is widely applied in customer feedback analysis, social media monitoring, and market research. Traditional sentiment analysis often fails to capture aspect-specific nuances, necessitating advanced machine learning and deep learning techniques. This paper reviews the evolution of ABSA from early machine learning models (e.g., SVM, Naive Bayes) to deep learning approaches (e.g., RNNs, LSTMs, CNNs) and state-of-the-art transformers (e.g., BERT, ALBERT, RoBERTa). Transformer models have shown a 12–18% accuracy improvement over traditional methods in benchmark datasets. Key challenges in multilingual adaptability, sarcasm detection, and domain-specific ABSA are discussed. Emerging trends, including hybrid models, zero-shot learning, and explainability techniques, demonstrate performance improvements. Recent studies (2023–2024) indicate that hybrid approaches enhance F1 scores by 5–10%, while zero-shot learning enables sentiment analysis in unseen domains with minimal labeled data. This review consolidates recent advancements, offering insights into ABSA methodologies, applications, and future directions, with a focus on model interpretability, efficiency, and multilingual scalability.