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Transparent Thin Film Transistors (TTFTs) have emerged as a promising technology for next-generation flexible electronics, transparent displays, wearable devices, biomedical sensors, and Internet of Things (IoT) applications due to their transparency, low power consumption, and compatibility with large-area fabrication processes. Despite their significant advantages, the long-term reliability and operational stability of TTFTs remain major challenges that limit their widespread commercial deployment. Device degradation caused by bias stress, thermal effects, environmental factors, and material defects can lead to threshold voltage shifts, mobility degradation, reduced ON/OFF current ratios, and shortened device lifetime. In recent years, Machine Learning (ML) and Deep Learning (DL) techniques have been increasingly adopted to predict device degradation, estimate lifetime, and model reliability characteristics. This review presents a comprehensive analysis of predictive analytics approaches for TTFT reliability enhancement using advanced machine learning and deep learning techniques. The study examines conventional machine learning models, deep neural networks, recurrent learning architectures, and hybrid intelligent systems employed for reliability prediction and degradation analysis. Furthermore, the strengths, limitations, and performance of existing approaches are critically evaluated with respect to prediction accuracy, interpretability, adaptability, and computational efficiency. The review identifies key research gaps, including the absence of explainable prediction frameworks, dynamic degradation monitoring models, and optimization-driven reliability improvement strategies. Finally, emerging research directions involving Explainable Artificial Intelligence (XAI), digital twins, physics-informed learning, and optimization algorithms such as Genetic Algorithms and Particle Swarm Optimization are discussed to support the development of next-generation reliability enhancement frameworks for Transparent Thin Film Transistors.