This survey explores the impact of dialects on Natural Language Processing (NLP) and the challenges and solutions in adapting NLP models to handle dialectic variations. The authors, from various institutions in Australia, Japan, the United Kingdom, and Germany, highlight the importance of considering dialects in NLP datasets and models to ensure equitable and fair language technologies. The survey covers a wide range of NLP tasks, including natural language understanding (NLU) and natural language generation (NLG), and discusses the performance degradation of NLP models on dialectic datasets. It also reviews past research in NLP for dialects, focusing on datasets, approaches, and specific languages such as English, Arabic, and German. The survey emphasizes the need for more diverse and representative datasets, the integration of dialect-aware models, and the importance of addressing social and ethical implications. The authors provide an overview of the current trends and future directions in NLP for dialects, aiming to guide researchers and practitioners in building more inclusive and equitable NLP systems.This survey explores the impact of dialects on Natural Language Processing (NLP) and the challenges and solutions in adapting NLP models to handle dialectic variations. The authors, from various institutions in Australia, Japan, the United Kingdom, and Germany, highlight the importance of considering dialects in NLP datasets and models to ensure equitable and fair language technologies. The survey covers a wide range of NLP tasks, including natural language understanding (NLU) and natural language generation (NLG), and discusses the performance degradation of NLP models on dialectic datasets. It also reviews past research in NLP for dialects, focusing on datasets, approaches, and specific languages such as English, Arabic, and German. The survey emphasizes the need for more diverse and representative datasets, the integration of dialect-aware models, and the importance of addressing social and ethical implications. The authors provide an overview of the current trends and future directions in NLP for dialects, aiming to guide researchers and practitioners in building more inclusive and equitable NLP systems.