  Traditional machine learning mainly supervised learning, follows the
assumptions of closed-world learning, i.e., for each testing class, a training
class is available. However, such machine learning models fail to identify the
classes which were not available during training time. These classes can be
referred to as unseen classes. Whereas open-world machine learning (OWML) deals
with unseen classes. In this paper, first, we present an overview of OWML with
importance to the real-world context. Next, different dimensions of open-world
machine learning are explored and discussed. The area of OWML gained the
attention of the research community in the last decade only. We have searched
through different online digital libraries and scrutinized the work done in the
last decade. This paper presents a systematic review of various techniques for
OWML. It also presents the research gaps, challenges, and future directions in
open-world machine learning. This paper will help researchers understand the
comprehensive developments of OWML and the likelihood of extending the research
in suitable areas. It will also help to select applicable methodologies and
datasets to explore this further.
