This paper presents the development of a neural network-based system designed to detect intentionality in dialogues, which refers to the goal-oriented aspects behind conversational exchanges. Intentionality plays a critical role in interpreting interpersonal communication by identifying the underlying intentions, whether explicit or subtle, in verbal interactions. Our research integrates state-of-the-art transformer-based large language models (LLM), such as DistilBERT, to classify and analyze intentional cues in dialogues. We cover the processes involved in data generation, model architecture, training methodology, evaluation metrics, and comparative benchmarking against systems like ChatGPT. The experimental results demonstrate the effectiveness of our approach in understanding and detecting nuanced intentional patterns, making our system a significant step forward in dialogue analysis technologies.