### Contributions  
This survey paper aims to offer targeted insights into meta-learning-enabled wireless communication systems to support the next generation of communication systems. This article represents a pioneering effort in the field, presenting a comprehensive study of meta-learning algorithms integrated with different wireless communication systems. Some of the major contributions of the article are listed as follows:  
* We introduce the novel meta-learning algorithm and discuss its potential to address the limitations of traditional machine learning methods in communication systems. The inherent advantages of meta-learning models surpass those of the current machine learning (ML) algorithms and can address significant existing challenges, thereby  
Figure 1: Use cases of artificial intelligence in wireless communication systems. extending the capabilities of intelligent networks. We presented a comprehensive overview of novel meta-learning algorithms and models, highlighting state-of-the-art solutions in the context of wireless communication systems. * We designed and simulated a meta-learning-enabled decoder to support three distinct wireless communication systems: radio frequency-based communication systems, optical wireless communication systems (OWCs), and molecular communication systems (MCSs). These three technologies were selected to demonstrate the versatility and effectiveness of meta-learning algorithms in various scenarios. Each wireless communication domain presents unique challenges due to differences in their channel models. OWCs transmit data through optical channels, which are prone to interference from various light sources and perform optimally with line-of-sight alignment. MCS channels, being diffusive in nature, pose their own set of challenges. Radio frequency-based wireless communication channels, on the other hand, are subject to multipath fading, signal attenuation, and interference from other electromagnetic sources. These channels require sophisticated techniques to manage frequency reuse and to mitigate issues like path loss and shadowing. Despite these differences, our results show that meta-learning can be effectively incorporated into each of these scenarios, outperforming conventional methods. We present a selection of original results to provide readers with a comprehensive understanding of the advantages and capabilities of meta-learning-enabled communication systems. These contributions significantly advance the field by demonstrating the applicability and benefits of meta-learning in diverse and challenging wireless communication environments. By addressing the unique characteristics and challenges of different communication systems, this work paves the way for more adaptable and efficient future communication networks. Driven by the fast-paced advancement in the fields of wireless communication systems, research in this domain is burgeoning. This section provides an overview of relevant research in the area of wireless communication systems and artificial intelligence. The author of [2] provides an introduction to key concepts in machine learning algorithms and their applicability to different domains of wireless communication. In [3], the authors consider a heterogeneous environment comprising different wireless access technologies and discuss different AI techniques that could facilitate such infrastructures by allowing self-reconfiguration, self-healing, and self-optimization. The potential of employing machine learning algorithms in different wireless communication scenarios has been studied in [5; 6; 7; 8; 9]. A recent paper on explainable AI (XAI) for 6G use cases highlighted the importance of transparency and interpretability in AI-driven communication systems [10]. The authors discussed various XAI techniques and their applications in 6G, emphasizing the need for explainable models to ensure trust and reliability in AI systems. Edge computing is an emerging paradigm that pushes the AI frontiers to the network edge, as opposed to centralized cloud computing.