<p>Panel time-series data, characterized by observations across multiple entities over time, have become essential in various fields such as economics, finance, and healthcare. Traditional statistical models such as Fixed and Random Effects have long been used to analyze panel data, addressing key challenges such as heterogeneity, endogeneity, and cross-sectional dependence. However, recent advancements in deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Temporal Fusion Transformers (TFT), offer new capabilities for capturing complex temporal and cross-sectional relationships. To the best of our knowledge, this is the first paper that surveys both statistical and deep learning models in panel data analysis. This survey provides a comprehensive comparison of these models, highlighting their respective strengths and weaknesses, with deep learning models excelling in handling nonlinear relationships, high-dimensional datasets, and time-varying effects. The paper also identifies key challenges in panel data forecasting and proposes future research directions, including hybrid modeling approaches and improvements in model interpretability. This work serves as a valuable guide for researchers and practitioners aiming to enhance forecasting accuracy in panel data applications.</p>