  The use of Machine Learning (ML) has rapidly spread across several fields,
having encountered many applications in Structural Dynamics and Vibroacoustic
(SD\&V). The increasing capabilities of ML to unveil insights from data, driven
by unprecedented data availability, algorithms advances and computational
power, enhance decision making, uncertainty handling, patterns recognition and
real-time assessments. Three main applications in SD\&V have taken advantage of
these benefits. In Structural Health Monitoring, ML detection and prognosis
lead to safe operation and optimized maintenance schedules. System
identification and control design are leveraged by ML techniques in Active
Noise Control and Active Vibration Control. Finally, the so-called ML-based
surrogate models provide fast alternatives to costly simulations, enabling
robust and optimized product design. Despite the many works in the area, they
have not been reviewed and analyzed. Therefore, to keep track and understand
this ongoing integration of fields, this paper presents a survey of ML
applications in SD\&V analyses, shedding light on the current state of
implementation and emerging opportunities. The main methodologies, advantages,
limitations, and recommendations based on scientific knowledge were identified
for each of the three applications. Moreover, the paper considers the role of
Digital Twins and Physics Guided ML to overcome current challenges and power
future research progress. As a result, the survey provides a broad overview of
the present landscape of ML applied in SD\&V and guides the reader to an
advanced understanding of progress and prospects in the field.
