Artificial Intelligence techniques for complex big data environments: methods and perspectives

In todays data-driven world, big data environments are becoming increasingly complex, characterized by high volume, variety, and velocity. Traditional data processing methods are no longer sufficient to handle such challenges. Artificial Intelligence (AI) provides powerful solutions for extracting value from diverse and dynamic data sources. This paper reviews key AI techniquesincluding machine learning, deep learning, natural language processing, graph-based models, and federated learningand discusses their applications in complex scenarios such as healthcare, finance, smart cities, and Industry 4.0. It also highlights major challenges, including data quality, model interpretability, computational cost, and privacy concerns. Finally, the paper explores future directions in AI development, such as multimodal learning and real-time decision-making. These advancements will play a vital role in enabling intelligent, efficient, and ethical data analytics in the years to come.