The Internet of Things (IoT) is a burgeoning networking technology that links both animate and inanimate devices worldwide. In an age where the IoTs is progressively incorporated into diverse sectors, including healthcare, tracking must be facilitated whilst diagnosing ailments for patients and healthcare practitioners. The Internet of Healthcare Things (IoHT)-based systems must secure healthcare data to prevent unauthorized access and intermediary attacks. However, identifying and categorising harmful software (malware) is still an open challenge, and no infallible method exists, especially in IoHTs settings. By contrast to many other research domains, standardized standards for malware discovery are difficult to identify. Malware compromises millions of devices and may execute many nefarious operations, including extracting personal information, encrypting data, and degrading system performance. Therefore, malware detection is essential for safeguarding computers and devices from malware attacks. This study examines recent advancements in malware detection across the IoTs and different systems via artificial intelligence (AI) algorithms, such as machine learning and deep learning. The following aspects are detailed in this work: definitions of malware and its types, the essential tools used in its analysis and detection, and the role of AI in handling challenges. This work also reviews the literature on the fields of detection and classification, with strengths and limitations in each.