  Machine learning (ML) provides algorithms to create computer programs based
on data without explicitly programming them. In business process management
(BPM), ML applications are used to analyse and improve processes efficiently.
Three frequent examples of using ML are providing decision support through
predictions, discovering accurate process models, and improving resource
allocation. This paper organises the body of knowledge on ML in BPM. We extract
BPM tasks from different literature streams, summarise them under the phases of
a process`s lifecycle, explain how ML helps perform these tasks and identify
technical commonalities in ML implementations across tasks. This study is the
first exhaustive review of how ML has been used in BPM. We hope that it can
open the door for a new era of cumulative research by helping researchers to
identify relevant preliminary work and then combine and further develop
existing approaches in a focused fashion. Our paper helps managers and
consultants to find ML applications that are relevant in the current project
phase of a BPM initiative, like redesigning a business process. We also offer -
as a synthesis of our review - a research agenda that spreads ten avenues for
future research, including applying novel ML concepts like federated learning,
addressing less regarded BPM lifecycle phases like process identification, and
delivering ML applications with a focus on end-users.
