This PhD project investigates the application of artificial intelligence techniques for the analysis and interpretation of complex data, with a focus on real-world use cases in education and agriculture. The research aims to develop intelligent systems that can personalize learning recommendations, identify students at risk of dropping out, monitor crop health, and detect early signs of agricultural diseases. By integrating Big Data analytics, machine learning models, and complex systems simulation, the project seeks to design practical tools that support data-driven decision-making across public and private sectors. Ultimately, the goal is to enable sustainable, accessible, and impactful solutions that foster digital transformation, regional innovation, and the evolution of smart, automated industries.