Artificial intelligence (AI) is reshaping the landscape of microbiological research, offering unprecedented tools for studying complex microbial interactions and optimizing the application of plant growth-promoting bacteria like Azospirillum. This chapter presents an integrative framework for applying AI to Azospirillum research, highlighting both conceptual advances and methodological pipelines. The first half of the chapter addresses theoretical challenges inherent to microbiome data analysis, including high dimensionality, compositionality, and multi-omics integration. It explores the application of machine learning and deep learning techniques for predictive modeling, phenotyping, and simulation-based optimization of bacterial inoculants. The second half offers a detailed, step-by-step experimental protocol for researchers aiming to implement AI-driven workflows, including batch effect correction, transfer learning, and federated learning across multiple field trials. Emphasis is placed on data standardization, variable harmonization, and reproducibility—key factors for ensuring that AI models are robust, interpretable, and biologically meaningful. This chapter serves as both a conceptual guide and a practical resource for microbiologists and agronomists seeking to integrate AI into Azospirillum-based sustainable agriculture.