  Background: Artificial intelligence (AI), with its vast capabilities, has
become an integral part of our daily interactions, particularly with the rise
of sophisticated models like Large Language Models. These advancements have not
only transformed human-machine interactions but have also paved the way for
significant breakthroughs in various scientific domains. Aim of review: This
review is centered on elucidating the profound impact of AI, especially deep
learning, in the field of gravitational wave data analysis (GWDA). We aim to
highlight the challenges faced by traditional GWDA methodologies and how AI
emerges 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, serving
as a sophisticated method to simulate and interpret the intricate patterns and
signatures of these cosmic phenomena. This modeling provides a deep
understanding of the astrophysical events that produce gravitational waves.
Next in line is GW signal detection, a refined technique that meticulously
combs through extensive datasets, distinguishing genuine gravitational wave
signals from the cacophony of background noise. This detection process is
pivotal in ensuring the authenticity of observed events. Complementing this is
the GW parameter estimation, a method intricately designed to decode the
detected signals, extracting crucial parameters that offer insights into the
properties and origins of the waves. Lastly, the integration of AI for GW
science has emerged as a transformative force. AI methodologies harness vast
computational power and advanced algorithms to enhance the efficiency,
accuracy, and adaptability of data analysis in GW research, heralding a new era
of innovation and discovery in the field.
