### Selection Method of Analyzed Articles Based on PRISMA Methodologies  
Before starting the discussion, defining the criteria used to select and exclude the most appropriate scientific articles is necessary. The adopted criteria are explicitly defined, considering many elements of the documents analyzed, such as applicability to the topics covered, relevance, publication year, and redundancy with other articles. This paper aims to offer the reader the broadest vision of IoT's current state of the art in smart manufacturing. The systematic selection of articles related to ML and IoT-based solutions in the smart manufacturing field is performed by adopting the PRISMA methodology, enabling readers to evaluate the reliability and applicability of the selected method [31; 32]. The selection process begins with an evaluation of the title to see if it contains references to the topics to be covered in this manuscript; it continues with an analysis of the abstract to see if the content is close to the issues to be covered; finally, we proceed to read and comprehend the manuscript under investigation. If the manuscript is unclear, the content is explored using other sources. The flowchart in Figure 3 illustrates the document selection process with the following steps: the title's relevance, the abstract's affinity, and the interest documents' overall content. Furthermore, this document selection methodology is applied to all the topics of this review paper. Figure 2: Overview of the manuscript’s treated topics. For an accurate analysis of smart manufacturing based on IoT applications in the fields mentioned above, the authors have analyzed 135 documents, divided into review papers, conference articles, research articles, and web sites. The main sources from which the articles were acquired are Elsevier, MDPI, IEEE, and Springer, with a small number of articles from other sources. The diagrams shown in Figure 4 highlight the distribution of the acquisition sources (Figure 4a) and the distribution of the types of articles analyzed (Figure 4b). Figure 3: Document selection method; (**a**) main keywords to filter the documents present in the literature; (**b**) description of articles selecting method with topics related to the presented review paper. Description and Comparison of Machine Learning Algorithms for Data Processing in Smart Manufacturing  
This section provides an overview of ML algorithms for data processing adopted in smart manufacturing, trying to report the main ML algorithms used by the authors. The selection of documents used to create this section was based on the search for articles and reviews that are mainly concerned with the topic of ML. Smart manufacturing is augmenting physical science with modern data analytics to improve decision making and system performance. As sensors and the IoT become more widely used, the need for large amounts of industrial data, rapid processing, and the ability to handle a wide variety of data becomes increasingly necessary [33]. Incorporating ML in manufacturing helps to increase efficiency and improve production operations. ML is a smart manufacturing technology that uses production data to improve industrial facilities' machine performance. ML is a subfield of artificial intelligence (AI) that uses statistical techniques to let computer systems learn from data without explicit programming. Numerous tasks, including prediction, classification, clustering, and optimization, may be carried out using machine learning [34]. ML programs identify focus areas in the production process to collect data and provide actionable insights for improvements. The program then provides algorithms with the data required to enable learning. Once this process is initiated, machines can continuously learn and improve processes independently without direct programming from human workers. By utilizing the data collected by machine learning programs, manufacturers can make more informed production decisions to drive improvement and business growth [34].