This survey explores the intersection of low-shot learning (LSL) and class imbalance, reviewing recent literature from January 2020 to July 2023. LSL involves learning to classify previously unseen classes with few or no examples, while class imbalance refers to datasets with significantly more instances of some classes than others. The survey finds that most works achieve performance at or above their respective state-of-the-art, highlighting current research gaps, especially those involving LSL techniques in imbalanced tasks. It emphasizes the lack of works utilizing LSL approaches based on large language models or semantic data, and works using LSL for big-data imbalanced tasks. The paper discusses common approaches to LSL, including prototypical learning and optimization-based meta-learning, and explores how LSL can be used to address class imbalance. It also covers various applications, such as image classification, object detection, and other image-data tasks, where LSL and class imbalance intersect. The survey identifies key challenges and opportunities for future research, including the development of more effective methods for handling class imbalance in LSL settings.This survey explores the intersection of low-shot learning (LSL) and class imbalance, reviewing recent literature from January 2020 to July 2023. LSL involves learning to classify previously unseen classes with few or no examples, while class imbalance refers to datasets with significantly more instances of some classes than others. The survey finds that most works achieve performance at or above their respective state-of-the-art, highlighting current research gaps, especially those involving LSL techniques in imbalanced tasks. It emphasizes the lack of works utilizing LSL approaches based on large language models or semantic data, and works using LSL for big-data imbalanced tasks. The paper discusses common approaches to LSL, including prototypical learning and optimization-based meta-learning, and explores how LSL can be used to address class imbalance. It also covers various applications, such as image classification, object detection, and other image-data tasks, where LSL and class imbalance intersect. The survey identifies key challenges and opportunities for future research, including the development of more effective methods for handling class imbalance in LSL settings.