This survey explores the challenges and approaches in natural language processing (NLP) for dialects of a language. It highlights the importance of dialects in NLP datasets and their impact on model performance. The survey covers a wide range of NLP tasks, including natural language understanding (NLU) and natural language generation (NLG), and discusses various languages such as English, Arabic, German, and others. It emphasizes that past research in dialects goes beyond simple classification and includes approaches like sentence transduction and integrating hypernetworks into LoRA. The survey also discusses the implications of dialectic differences for the equity of language technologies and the need for fair and inclusive NLP practices. It highlights the challenges posed by dialects, such as differences in syntax, vocabulary, and pragmatics, and the impact of dialects on NLP tasks like sentiment analysis, machine translation, and speech recognition. The survey also discusses recent trends in dialect-aware NLP, including the use of dialect-aware models and the importance of dialectic datasets in evaluating NLP performance. It covers various approaches to dialect identification, sentiment analysis, morphosyntactic analysis, and parsing, and highlights the need for further research in this area to ensure equitable and inclusive language technologies. The survey also discusses the importance of considering dialectic variations in NLP benchmarks and model architectures to improve the performance of NLP systems for different dialects.This survey explores the challenges and approaches in natural language processing (NLP) for dialects of a language. It highlights the importance of dialects in NLP datasets and their impact on model performance. The survey covers a wide range of NLP tasks, including natural language understanding (NLU) and natural language generation (NLG), and discusses various languages such as English, Arabic, German, and others. It emphasizes that past research in dialects goes beyond simple classification and includes approaches like sentence transduction and integrating hypernetworks into LoRA. The survey also discusses the implications of dialectic differences for the equity of language technologies and the need for fair and inclusive NLP practices. It highlights the challenges posed by dialects, such as differences in syntax, vocabulary, and pragmatics, and the impact of dialects on NLP tasks like sentiment analysis, machine translation, and speech recognition. The survey also discusses recent trends in dialect-aware NLP, including the use of dialect-aware models and the importance of dialectic datasets in evaluating NLP performance. It covers various approaches to dialect identification, sentiment analysis, morphosyntactic analysis, and parsing, and highlights the need for further research in this area to ensure equitable and inclusive language technologies. The survey also discusses the importance of considering dialectic variations in NLP benchmarks and model architectures to improve the performance of NLP systems for different dialects.