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Background: Artificial intelligence (AI), with its vast capabilities, hasbecome an integral part of our daily interactions, particularly with the riseof sophisticated models like Large Language Models. These advancements have notonly transformed human-machine interactions but have also paved the way forsignificant breakthroughs in various scientific domains. Aim of review: Thisreview is centered on elucidating the profound impact of AI, especially deeplearning, in the field of gravitational wave data analysis (GWDA). We aim tohighlight the challenges faced by traditional GWDA methodologies and how AIemerges as a beacon of hope, promising enhanced accuracy, real-time processing,and adaptability. Key scientific concepts of review: Gravitational wave (GW)waveform modeling stands as a cornerstone in the realm of GW research, servingas a sophisticated method to simulate and interpret the intricate patterns andsignatures of these cosmic phenomena. This modeling provides a deepunderstanding of the astrophysical events that produce gravitational waves.Next in line is GW signal detection, a refined technique that meticulouslycombs through extensive datasets, distinguishing genuine gravitational wavesignals from the cacophony of background noise. This detection process ispivotal in ensuring the authenticity of observed events. Complementing this isthe GW parameter estimation, a method intricately designed to decode thedetected signals, extracting crucial parameters that offer insights into theproperties and origins of the waves. Lastly, the integration of AI for GWscience has emerged as a transformative force. AI methodologies harness vastcomputational power and advanced algorithms to enhance the efficiency,accuracy, and adaptability of data analysis in GW research, heralding a new eraof innovation and discovery in the field.

Dawning of a New Era in Gravitational Wave Data Analysis: Unveiling Cosmic Mysteries via Artificial Intelligence -- A Systematic Review