Magnetic Resonance Imaging (MRI) is a fundamental diagnostic imaging modality that provides excellent soft-tissue contrast without ionizing radiation. However, MRI image quality is frequently degraded by noise, intensity non-uniformity (bias field), low spatial resolution, and motion artifacts, which adversely affect diagnostic accuracy and the performance of downstream artificial intelligence (AI) applications. This review presents a systematic and comparative assessment of MRI image enhancement techniques using a structured literature screening methodology inspired by the PRISMA framework. Unlike previous reviews that primarily summarize individual enhancement approaches, this study proposes a unified classification framework encompassing traditional image processing, deep learning (DL)-based, and hybrid enhancement techniques. A cross-paradigm comparison is performed using common evaluation dimensions, including enhancement accuracy, computational complexity, data requirements, generalization capability, interpretability, hardware dependency, and clinical readiness. The review further examines representative algorithms, benchmark datasets, quantitative performance metrics, clinical validation studies, regulatory pathways, and current challenges such as domain shift, explainability, and reproducibility. Emerging trends, including transformer-based architectures, self-supervised learning, multimodal enhancement, federated learning, and edge AI, are also discussed. The analysis indicates that although DL approaches consistently achieve superior quantitative performance, hybrid techniques provide a more balanced trade-off between enhancement accuracy, interpretability, computational efficiency, and deployment feasibility. This review offers a comprehensive reference by integrating methodological, technical, and clinical perspectives while identifying key research directions for developing robust, trustworthy, and clinically deployable MRI image enhancement systems.