This comprehensive survey on LLMs-as-Judges paradigm examines the framework across five dimensions: functionality, methodology, applications, meta-evaluation, and limitations. It discusses how LLMs as judges evaluate outputs or components of AI applications for quality, relevance, and accuracy, providing scores, rankings, categorical labels, explanations, and actionable feedback. These outputs enable users to refine AI applications iteratively, reducing dependency on human annotations using interpretable explanations. The survey breaks down the concept into five dimensions, including functionality, methodology, applications, meta-evaluation, and limitations, highlighting its advantages, limitations, and methods for evaluating its effectiveness. It also explores three main input types for evaluation: pointwise, pairwise, and listwise, as well as various criteria for assessment, such as linguistic quality, content accuracy, task-specific metrics, user experience, reference-based vs. reference-free evaluation, and applications across diverse fields like summarization, multimodal models, and domain-specific use cases. Despite their promise, LLM judges face notable challenges, including bias, domain expertise limitations, prompt sensitivity, adversarial vulnerabilities, and resource intensity. The paper suggests strategies to mitigate these limitations, such as regularly auditing for bias, incorporating domain experts, standardizing prompt designs, combining human oversight with automated evaluation systems, and aligning application-specific criteria with stakeholder goals. Overall, the survey underscores the transformative potential of LLMs as evaluators while emphasizing the importance of addressing their limitations, highlighting the need for robust, scalable evaluation frameworks in AI systems.