The purpose of this research is to use deep learning and feature selection methods to discover potential causes of thyroid illness. We use the UCI DL Repository dataset, which has 2,800 occurrences and 28 characteristics, and we use Boruta and Recursive Feature Elimination (RFE) techniques to carefully preprocess and refine it in order to extract relevant features. Results show that RNN achieved an exceptional recall rate of 96.01% and accuracy scores above 98% after extensive examination across six DL algorithms, including Autoencoder and Long Short Term Memory Networks. Although there have been some accomplishments, there are still obstacles. One of these is that Multilayer perceptron continually has lower accuracy levels. In healthcare analytics, where even small gains in recall and accuracy can have a huge effect on diagnostic performance and patient outcomes, our results highlight the vital importance of strong preprocessing and feature selection methods. To further improve the accuracy of classification and the refinement of thyroid disease risk factor identification, future studies may investigate hybrid model architectures and innovative feature engineering techniques.