This article offers a detailed survey and comparative analysis of various deep learning models, including CNNs, RNNs, LSTMs, and GRUs. It assesses their applications and effectiveness across different sectors, contributing significantly to understanding deep learning advancements.

Survey Highlights:

• Detailed structure and utility of each deep learning model are discussed, highlighting domain-specific applications.
• Comparative analysis based on publicly available datasets illustrates the performance variances between different models.
• This comprehensive review aids in selecting appropriatе deep learning strategies based on specific needs and constraints within various domains.

Importance of Study:

• The insights provided enable developers and researchers to optimize model choices for enhanced performance and accuracy in real-world applications.
• Understanding the nuances and capabilities of each model facilitates better design and implementation of AI solutions across industries.

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