Understanding animal decision-making processes in controlled environments is crucial for gaining insights into cognitive functions and behavior. T-maze tasks, commonly used in behavioral experiments, provide a valuable framework for studying binary decision-making, where animals must choose between two distinct paths. This study presents a comprehensive computational analysis of animal behavior in T-maze tasks, where animals are required to make binary decisions between two paths. By integrating both analytical modeling and machine learning techniques, we aim to explore the decision-making processes involved in such behavioral tasks. In particular, we focus on enhancing our understanding of the cognitive mechanisms that guide these decisions by employing advanced deep learning models. Specifically, we apply Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) architectures to analyze the data. These models achieved impressive classification accuracies of 92.3% and 95.6%, respectively, demonstrating their superior predictive capabilities compared to traditional machine learning methods such as Support Vector Machines and Random Forests. In addition to machine learning, we employed analytical methods to model and interpret the decision-making behavior of animals, providing deeper insights into their cognitive processes during navigation. The results of this research contribute to the field of computational ethology by showcasing the potential of combining analytical solutions with machine learning techniques to model complex biological systems. This approach offers new avenues for understanding animal decision-making in T-maze tasks and similar behavioral experiments.