Kokkirigadda Ratna Priyanka,Mr. Nandana Kumar 03 09 Music streaming platforms have transformed listening from an albumoriented activity into a continuously personalized datadriven experience Spotify generates a rich behavioural footprint involving listening histories track characteristics artist preferences playlist interactions temporal patterns discovery activity and contextual signals However existing research frequently treats music consumption as separate problems such as recommendation popularity prediction user profiling or audiocontent analysis This review synthesizes recent research on musicstreaming behaviour with particular emphasis on Spotify data and identifies the limitations of isolated analytical approaches Six interrelated dimensions are examined multisource musicbehaviour representation multitemporal user preference modelling contentdriven and collaborative recommendation contextual social and newrelease discovery explainability diversity and popularity bias and adaptive learning for changing user preferences Recent Spotify research demonstrates that generalized user representations can combine multimodal signals across different temporal scales and support multiple downstream personalization tasks while studies of newrelease and socially motivated listening demonstrate that recommendation relevance depends on content age community culture and timing Based on this synthesis this review develops a Unified Spotify Behaviour Analytics Framework USBAF consisting of data acquisition behavioural representation intelligence adaptive explanation and insight layers The review concludes with open challenges and a staged roadmap toward contextaware explainable diverse continually adaptive and generative musicstreaming intelligence Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026