In a hospital, keeping track of patients and their needs is a multi-faceted nightmare. Each department has its own intake and check-out process, and all of this information must be in one central place. That’s why there are so many hospitals that are struggling to confirm the organization of patient data. There are several of Machine Learning algorithms that can help to optimize patient flow in hospitals, provide a real-time presentation of department workflow. This editorial will examine some of the most effective ML algorithms to solve these issues. Before we dive directly into the solutions, it is important to understand how ML can help specifically in the healthcare industry. The increased adoption of ML stipulates a wide range of new technologies and applications that would transform medicine. These healthcare developments would help providers to offer care tailored to the individual needs of patients, making treatment more effective and affordable. Although the actual impact of the algorithms on healthcare is still a subject of future research, the expected outcomes are huge. Before analyzing how they work, it is important to explain the terminology. Specifically: data mining is the manner of discovering new data or knowledge from huge amounts of data. The major focus of data mining is made on unstructured data, as e.g. medical records, social media posting or images. Machine learning implies a computer program that is trained on data to find patterns and make predictions.1 There are many types of ML algorithms, but they all have three steps in common: • Training: create a model using the data. • Use the model to make predictions. • Tune the model to get the best results. Natural Language Processing (NLP) is a type of machine learning that allows computers to understand human language.2 Health care providers use NLP to meet patients’ needs, goals, and expectations. One of the many problems in hospitals is the inability to manage patient flows through various departments and check-out points. The problem is that when patients arrive at a hospital, they can be instructed to go to the Emergency Department. They can also be told to go to a different department based on the results. In spite of the fact that it is necessary for there to be a single source of information, regardless of the department, in order to ensure that both patients and staff have access to the same level of information.3 It would be ideal to have a centralized location that provides information on the department's current availability, wait times, and staff members' status. In order to enhance the efficiency of patient flow within the hospital, administrators and healthcare providers can utilize machine learning algorithms to gather information regarding patients' arrival, departure, and current condition. In addition, it is able to evaluate the condition of each individual patient and give staff with information in real time. This strategy makes it easier to control the flow of patients in an effective manner, which in turn enables staff members to concentrate on the activities that are most important to them. There are a great number of data sources that can be operated in order to collect information regarding the conditions of patients. To ensure that activities are carried out in the most effective manner possible, it is essential that staff members have instant access to these resources. Natural language processing, often known as NLP, is a method that is utilized for the retrieval of information that is very effective. Natural Language Processing (NLP) has made it possible for computers to grasp human language. This capability was first introduced in 2005. They are able to collect vital information from a patient's report and incorporate it into the centralized system in a seamless manner.4 Because of this, the system is able to collect and store information about the diagnoses, treatments, prescriptions, and other pertinent information pertaining to patients. In addition to providing patients and staff members with real-time updates on the situation, the data may be employed to provide precise estimations of the amount of time that patients will be required to wait. In addition to this, it is able to recognize and assess patterns and trends within the process of the department, thus allowing the required adjustments to be made in order to improve efficiency. After collecting data from patients, it is possible to analyses trends and patterns within the data. The investigation has the potential to improve patient care by highlighting areas for improvement. Using an acceptable sample size, it is possible to make reliable predictions about the likelihood of specific occurrences. The use of a machine learning algorithm allows for the assessment of the likelihood that persons with specified conditions will be discharged or admitted to a medical facility.5 The use of these algorithms is critical in giving invaluable assistance to workers in the performance of their everyday activities, as well as predicting and anticipating the state of patients. The rapid examination of data has various advantages, notably in terms of minimizing wait times. The provision of timely help is an important part of controlling patient flow because it has a direct impact on patient satisfaction and likely to recommend the healthcare institution. The implementation of an automated system for data collection and analysis can assist hospitals in attaining transparency. To augment transparency within the healthcare facility, administration may elect to employ machine learning algorithms to produce status dashboards.6 This type of dashboard has the capability to deliver instantaneous updates regarding the condition of facilities and departments. It can provide details such as staff availability, wait times, and the individual patient loads of each staff member. There are specific situations in which the implementation of a decentralized system is required to assure hospital-wide transparency. Particularly designed chatbots may be employed in such circumstances to collect data and provide immediate transparency. A chatbot that is purpose-built for the hospital environment can efficiently respond to fundamental inquiries, collect critical data, and improve patient transparency. By employing ML algorithms, the efficacy of patient flow can be significantly enhanced. Conflict of interest: None declared.