Markov chains, semi-Markov chains, and more broadly, Markov processes and their hidden variants constitute a rich and versatile family of stochastic models with applications spanning a wide range of fields, including population dynamics, epidemic modeling, seismology, speech and activity recognition, and reliability analysis. Despite their widespread use, ongoing theoretical and algorithmic advancements are essential to tackle emerging challenges in real-world problems, such as those involving complex observations, multiple interacting hidden dynamics, or control scenarios with constraints. These models have been explored from diverse perspectives across methodological communities—such as process statistics, computational statistics, and optimal control—and applied domains, including signal processing, ecology, and medicine. As part of the ANR HSMM-INCA project, the national workshop PMSMA was organized in 2023 to bring together the French statistical community engaged in these topics. The MASEMO workshop has two key objectives: expanding PMSMA scope to an international audience and extending its thematic range. Hidden Markov and semi-Markov models are often developed independently within non-statistical communities, such as signal processing and artificial intelligence. The upcoming workshop seeks to bridge these gaps by fostering dialogue and collaboration across disciplines, particularly on topics at the intersection of these fields. We hope the event will provide a framework to share the latest developments, encompassing theoretical insights, modeling techniques, inference methods, decision-making strategies, and practical applications.