Dialogue systems, also known as conversational AI or chatbots, are an important research area in natural language processing that focuses on enabling machines to understand, generate, and maintain coherent conversations with humans. Early systems relied on rule-based or retrieval-based approaches, while modern research primarily leverages deep learning techniques such as sequence-to-sequence models, attention mechanisms, and transformers (e.g., GPT, BERT-based conversational models) to generate context-aware and fluent responses. Recent advances explore task-oriented dialogue systems for goal-driven interactions, open-domain chatbots for natural conversation, reinforcement learning for dialogue policy optimization, and multimodal dialogue incorporating text, speech, and visual inputs. Applications span customer service, virtual assistants, healthcare support, education, and entertainment, demonstrating the practical utility of dialogue systems in real-world scenarios. Current research also emphasizes personalized dialogue, context tracking, handling ambiguity, and incorporating commonsense and external knowledge to improve the coherence, relevance, and safety of generated responses.