In recent years, the rapid growth of research on Large Language Models (LLMs) has led to a surge in survey papers, making it difficult for beginners to take in all the latest advancements. This report aims to overcome this challenge by analyzing a collection of LLM survey papers to look at key trends and provide useful insights into the field. We aggregate a dataset containing titles, summaries, categories, and release dates of LLM survey papers, then preprocess the data and apply a Random Forest Model to predict the categories of surveys based on their textual content. Our evaluation demonstrates the use of Random Forest Classifiers, achieving a precision of 0.70 but an accuracy of 0.38, highlighting room for improvement. Overall, this work contributes to making LLM research more accessible by offering a structured exploration of trends in survey publications and laying the foundation for automatic categorization of future research surveys.