Sophisticated software analysis techniques are necessary for the security of current IoT systems. Static analysis is one such technique that has consistently proven useful. The labor of human specialists needs to be automated and intellectualized since the connections between IoT systems are becoming more complicated, larger, and more heterogeneous. Therefore, we postulate that machine-learning techniques can be useful for static analysis of IoT systems. The study’s ontology is reflected in the research plan, which seeks to validate the hypothesis. The most important things that this work has accomplished are: Streamlining the process of static analysis for IoT systems and formalizing model decisions for ML problems; reviewing and analyzing a large body of literature in the field; validating that machine learning tools are appropriate for each step of static analysis; and proposing a concept for an intelligent framework to aid in static analysis of IoT systems. The findings are groundbreaking because they formalize the processes and solutions as “Form and Content,” examine each stage from the viewpoint of the complete suite of machine-learning solutions, and account for the entire static analysis process (beginning with the research of IoT systems and ending with the delivery of the results).