###### Abstract
Various parameters and factors can directly or indirectly impact the TCO of CWPs. Hence, identifying these parameters is crucial for TCO estimation and forecasting. Previously, the components of TCO (e.g., operating cost, energy cost, and maintenance cost) estimations and forecasting primarily relied on non-data-driven and non-machine learning (ML)-based strategies and statistical analysis, necessitating extensive data and expert calibration [46]. The models were built using programs such as Energy Plus and the Transient System (TRNSYS) simulation tool [11; 47; 48; 49; 50; 51] based on comprehensive sets of physics-based equations [49; 52; 53; 54]. However, these models often face limitations in terms of accuracy due to data accessibility issues and in practicality due to their high level of complexity [49; 52; 53; 55]. Thus, adopting an accurate and practical strategy for predicting TCO is essential. Given the importance of accurate TCO forecasting and the limitations of the methods above, the role of AI becomes vital [56]. Data-driven AI models that leverage historical data have shown promising results in various domains, including TCO prediction and optimization [50; 57; 58; 59; 60]. Accurate TCO prediction is essential for every business, including CWPs, because it helps the management team set budget estimates and allocate resources effectively throughout the project's life cycle. Controlling or optimizing TCO becomes challenging if the prediction model relies on inaccurate cost estimations [58; 61]. Therefore, to overcome these challenges, AI-based solutions have attracted attention in recent years [58; 62; 63]. The advancements in hardware technologies [64; 65; 66; 67] have enabled the application of sophisticated AI algorithms to analyze historical data reflecting complex system behaviors, such as HVACs, allowing for the forecasting or predicting future scenarios [68; 69]. This AI-driven approach offers substantial financial benefits to the owners of such systems in the long term [70]. AI algorithms, especially deep learning (DL)-based solutions, have proven their effectiveness in challenging time series forecasting scenarios,autonomously handling dynamic and unstructured data without human expert supervision [71; 72; 73; 74; 75; 76]. AI and ML algorithms have showcased success in dealing with complex correlations among parameters in dynamic scenarios [77; 78; 79; 80]. For instance, they have been employed in multiple aspects, such as predictive control optimization, electricity demand forecasting, and maintenance of chiller and HVAC systems [81; 82; 83]. Similarly, AI and ML techniques can aid TCO prediction and optimization of CWPs by analyzing real-time historical, nonlinear, and unstructured data. Utilizing data-driven AI-based solutions for TCO estimation in CWPs is essential, considering their ability to process large amounts of historical data efficiently, resulting in more accurate predictions and informed decision making regarding system acquisition and operation [84; 85; 86]. An overview of the general framework for predicting and optimizing key parameters affecting the TCO of CWPs using AI-based forecasting models is provided in Figure 1.  
Recently, a few studies have investigated the role of AI in ensuring energy efficiency in building management [84; 87] and various energy systems [46; 68; 88], yet a comprehensive analysis of the critical parameters and role of AI related to TCO prediction of CWPs is limited. It is also necessary to explore the recent state-of-the-art studies incorporating AI methods to forecast or predict meaningful parameters that ultimately lead us to efficiently and accurately optimize the TCO of CWPs for the future. To the authors' best knowledge, no previous study has conducted a comprehensive systematic literature review covering TCO prediction methods, along with a holistic analysis of the contributing factors and parameters related to CWPs' expenses.