Sentiment analysis (SA) helps in interpreting opinions, customer reviews, and feedback by determining the emotional tone of a text. However, traditional SA algorithms frequently produce inaccurate results because they are unable to discern specific aspects of feelings and contextual elements, which makes it difficult for them to analyze text effectively. The sophisticated technique known as Aspect-Based SA (ABSA) focuses on sentiments expressed toward specific aspects of a topic, enabling a more accurate and comprehensive interpretation of emotions. Notwithstanding their advantages, ABSA models face challenges like feature sparsity and inadequate contextual awareness, which make sentiment classification challenging in complex or perplexing scenarios. Machine learning (ML), Deep Learning (DL) and ensemble approaches can effectively lessen the difficulties associated with ABSA. These models improve sentiment classification performance by incorporating complex contextual connections and learning large feature representations. ABSA models constructed with ML, DL, and ensemble techniques are thoroughly examined in this article. It evaluates their methods, advantages, limitations, datasets, and performance metrics and suggests potential future directions.