## 1 Introduction
At the moment, real-time and proactive maintenance requires real-time sensing and extensive data collection, and it is mostly the evolutionary step of condition-based maintenance [7]. PdM is showing great potential when guided by an ML algorithm, which works in the domain of artificial intelligence. The effectiveness of ML is measured by its ability to rapidly handle big data. Using ML we can \"interpret\" complex processes, assets, and data. ML can give direct access from machines to a large number of sample data. In agent-based implementations, code is included inside the internal machine engine in order to detect patterns and trends while triggering alarms and stoppages based on tasks, products, and boundaries. Rather than programming computers to be smart by hand-coding software routines with a specific set of instructions to accomplish a particular task, ML examines the relationship between a data record and the labelled output (e.g., failures) and then creates a data-driven model to report future outcomes. This can recognize patterns from historical events and can forecast or prevent failures based on learnings from specific breakdown of root causes. Cloud-based AI systems continuously learn from alerts. In this way, it results in performance optimization and consequent increases in machine availability. Comparing an AI-based approach to traditional condition monitoring or more classical maintenance strategies, such as usage-based exchange, a considerable improvement can be expected due to better failure prediction. ML algorithms are being applied to a broad range of problems or applications in the manufacturing domain. They can generally used to enlarge and extract prediction while mitigating the difference in training and test sets. They generally contain an evolution of the condition-based maintenance [12]. Open source, pre-made beta tools are widely available on the internet, further promoting adoption. However, they suffer problems with virtual prototyping and data collection. Supervised and unsupervised learning, inductive logic programming, clustering, reinforcement learning, Bayesian networks, and decision tree learning are some major proposals for ML. There is no clear border between ML and deep learning (DL). In the most basic terms, DL is a probability-based decision system. Analysing the dataset, DL constructs the probability network by making statements, decisions, or predictions with a risk or more \"certain\" outcome projection. This study analysed the recent literature on ML applications for PdM to construct patterns and guidelines for situational intelligence, performance improvement, and reliability improvement in smart manufacturing [13]. The question of what is the best ML for PdM comes from what best fits the data and objective [14; 15]. Classifying the maintenance strategies in order to increase complexity, it is possible to recognize strategies for (_i_) **Corrective** or **Reactionary Maintenance** (CM or RM). In CM or RM the asset operates on breaking-down followed by maintenance activities with the aim of speedily restoring; this procedure passively acts after failure to detect and repair. According to ISO 13306:2010, CM maintenance is carried out after fault recognition and is intended to put an item into a state in which it can perform a required function. It is also referred to as run-to-failure maintenance, in which you cannot run without spare parts. It is the simplest maintenance approach used on constant failure rate. Moreover, it is the least effective one, in relation to the cost of interventions and the associated downtime after failure [16]. (_ii_) **Preventive Maintenance** (PM). PM anticipates breakdowns so that they can be managed and failures can be reduced. According to ISO 13306:2010, PM is carried out at predetermined intervals or according to prescribed criteria and intended to reduce the probability of failure or the degradation of the functioning of an item. Adjustments, replacements, renewals, and inspections are organized based on planning and scheduled tasks.