Abstract <p>This study addresses foundational challenges in artificial intelligence (AI) arising within the context of multidisciplinary research and explores methodologies for their resolution through the application of machine learning and neural network techniques. A systematic approach is emphasized as a critical framework for accurately formulating problems, particularly in disciplines characterized by low formalization, such as geology and ecology. The research delineates core AI tasks, including retrodiction, forecasting, search optimization, and design synthesis, and discusses solutions grounded in advanced methodologies such as clustering algorithms and regression modeling. A significant focus is placed on the integration of explainable artificial intelligence (XAI) frameworks, which enhance model interpretability, facilitating nuanced insights into complex processes inherent in interdisciplinary investigations. The study also highlights the application of holotypic algorithms, which demonstrate efficacy in resolving classification and object recognition challenges via multidimensional data analysis. This work underscores the transformative role of AI in automating research workflows and optimizing the efficiency of scientific endeavors across interdisciplinary domains.</p>