###### Abstract  
The Digital Twin (DT) concept in the manufacturing industry has received considerable attention from researchers because of its versatile application potential. Machine Learning (ML) adds a new dimension to DT by enhancing its functionality. Many studies on DT in the manufacturing industry have recently been published. However, there is still a lack of a systematic literature review on different aspects of ML-based DT in the manufacturing industry from a bibliometric and evolutionary perspective. Therefore, the proposed study is mainly aimed at reviewing DT in the manufacturing industry to identify the contribution of ML, current methods, and future research directions. According to the findings, the contribution of ML to this domain is significant. Additionally, the results show that the latest ML technologies are being used in the DT domain; neural networks have evolved based on application-specific requirements. The total number of papers and citations per paper on ML-based DT is increasing. The relevance of ML in DT has increased over time. The current trend is to use ML-based DT for data analytics. Additionally, there are many unfilled gaps; certain gaps include industrial applications of DT, synchronisation with real-time data through sensors, heterogeneous data management, and benchmarking. advised manufacturing: digital twin; machine learning: bibliometric analysis; evolutionary analysis +
Footnote †: journal: [PERSON]  
as the person who first coined the term DT in 2002 [11]. [PERSON] described DT as \"The Digital Twin is a set of virtual information constructs that fully describes a potential or actual physical manufactured product from the micro atomic level to the macro geometrical level\". The DT concept has received popularity since then, and other definitions have been published. [PERSON] and [PERSON] [12] described DT as a means of converging physical and virtual spaces. [PERSON] and [PERSON] [13] described DT as a representation of a physical object which merges cyberspace and physical space through near-real-time synchronisation. According to [PERSON] and [PERSON] [14], DT is a simulation of physical space. [PERSON] and [PERSON] [15] described DT as an ultra-high-fidelity simulation of its physical counterpart. An ML-based DT can be defined as \"an application-specific DT which is comprised of components such as physical entity, ML model, ML data, real time synchronisation, IoT and used for tasks such as process control, scheduling and prediction\" The differences between ML-enabled DT and AI-enabled DT are  
* ML-enabled DT is a subset of AI-enabled DT. * ML-enabled DT involves algorithms such as ANN, RF, kNN, whereas AI-enabled DT involves algorithms such as genetic algorithm, ant colony optimization, and particle swarm optimization, in addition to ML algorithms. * ML-enabled DT is primarily used for process control, scheduling and prediction, whereas AI-enabled DT is primarily used for optimization, scheduling, and resource allocation. * ML-enabled DT is more abundant than AI-enabled DT  
[PERSON] defined a three-dimensional DT architecture with a set of tests called the test of virtuality (GTV) to examine the fidelity of a DT. These three dimensions are (a) physical entity (PE), (b) virtual entity, and (c) the connection between physical and virtual worlds. However, [PERSON] did not define any auxiliary technology needed to build DT. With the advancement of sensor technology, IoT, and the introduction of big data and ML, DT architecture has evolved into a five-dimensional architecture. These five dimensions are (a) PE, (b) virtual entity, (c) services, (d) Digital Twin data, and (e) connection [2]. These five dimensions have evolved over time to eight dimensions: (a) integration breadth, (b) connectivity mode, (c) update frequency, (d) CPS intelligence, (d) simulation capabilities, (e) digital model richness, (f) human interaction, and (g) product lifecycle according to CIRP Encyclopaedia [16].