The detection and classification of pleural effusion present significant challenges in clinical practice, often contributing to delayed diagnoses and suboptimal patient outcomes. Recent advancements in artificial intelligence (AI) and machine learning (ML) techniques hold substantial promise for enhancing the accuracy and efficiency of pleural effusion diagnostics. This paper reviews the current landscape of AI applications in pleural effusion detection, synthesizing findings across diverse studies to illustrate the transformative potential of these technologies. We examine various ML models, including deep learning and ensemble methods, that leverage clinical, laboratory, and imaging data to improve diagnostic performance. Notably, models such as Light Gradient Boosting Machine (LGB) and XGBoost have achieved accuracy levels up to 96% and high AUC values (e.g., AUC = 0.883 for pleural effusion differentiation). This overview highlights the importance of integrating diverse diagnostic parameters to enhance pleural effusion diagnostic accuracy and outlines future research directions essential for optimizing patient management and outcomes.