ResNet-18 is a variant of the residual networks (ResNets), and it has become the most popular architecture in deep learning. The work comprises a comprehensive review of the evolution, design improvements and application landscape in different domains for ResNet-18 architecture. The paper also mentions some relevant works that proposed different ResNet-18 variants and practical usages which helps indicate the diversity and power of this model. Through a detailed exploration of recent breakthroughs and diverse applications that utilize it, this review attempts to enlighten the deep learning community about the overall effect ResNet-18 has made. ResNet-18 has been widely adopted in academia and industry due to its robustness, simplicity. It also explores the fusion of ResNet-18 with some other technologies and its ability to lead the way for new research avenues. These contributions are presented for multiple domains, which include medical imaging, agriculture, remote sensing and biometrics, showcasing how the usefulness of ResNet-18 is tailored. The methodical development of ResNet-18 using improvements and adaptations has guaranteed that it is still applicable, today in new machine learning as well as computer vision difficulties. In summary, we reviewed the existing ResNet-18 from multiple perspectives and in-depth awareness of its significant discoveries as well prospects beyond this.