Artificial intelligence (AI) is transforming nanoscience by enabling data-driven prediction, discovery, and design of nanomaterials and nanobio interfaces. Because nanobio interactions involve complex physicochemical and environmental factors, experimental exploration is often costly and time-consuming. AI methods provide powerful tools to model these multidimensional relationships and predict nanomaterial properties and biological interactions. Recent advances in machine learning (ML), representation learning, and large language models (LLMs) have further expanded AI capabilities in nanotechnology. Classical ML models remain widely used for structured datasets, while encoding models and LLM-based systems enable knowledge extraction, reasoning, and automated research workflows. Although some of these approaches have already been applied in nanomaterial and nanobio interface research, others remain emerging technologies with significant potential for future applications. This tutorial reviews AI methodologies for nanomaterial and nanobio interface research across three paradigms: AI for Prediction, AI for Discovery, and AI for Design. We also discuss practical considerations such as model explainability, generalization, and data quality to guide responsible and effective applications of AI in nanoscience. This tutorial aims to provide practical guidance for leveraging AI to accelerate innovation in nanomaterial and nanobio interface research, while fostering a balanced understanding of both the strengths and limitations of current AI methodologies.