###### Abstract  
This study investigates key parameters and applications of artificial intelligence (AI) in predicting the total cost of ownership (TCO) for chilled water plants (CWPs). Forecasting the TCO of CWPs is challenging due to the diverse and dynamic factors and parameters that influence it, necessitating understanding their complex correlations and causations. While AI and non-AI approaches have improved parameter prediction accuracy in different engineering applications, comprehensive literature reviews on chiller TCO prediction methodologies and their influencing factors are limited. This systematic review addresses three objectives: (1) to identify the key parameters in estimating TCO of CWPs, (2) to examine the existing techniques employed in TCO forecasting and their benefits in energy and cost savings, and (3) to evaluate how AI enhances TCO prediction accuracy and robustness. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, this review analyzed studies from 2017 to 2024 sourced from the Web of Science and Scopus databases. This study identifies several key parameters influencing TCO, including cooling load, energy consumption, chiller capacity, and the Coefficient of Performance (COP). The review shows that AI-driven models, such as deep learning and machine learning algorithms, have improved the accuracy and robustness of TCO predictions, and it further demonstrates scenarios where AI outperforms conventional prediction and forecasting methods. Notably, the current review shows that AI techniques are predicted to be capable of reducing total life cycle costs by up to 18%, based on modeling estimates. machine learning; predictive modeling; energy consumption; cooling; cost savings 2020 IEEE/ACM Transactions on Intelligent Systems (IET) 2020 IEEE/ACM Transactions on Intelligent  
of HVAC systems [12; 13]. These systems play a vital role in cooling and dehumidifying indoor air by circulating chilled water throughout the building [14; 15]. Chiller plants are commonly installed in medium- to large-sized buildings, where their higher energy efficiency offers a noteworthy advantage over smaller air-cooling units [16; 17]. Globally, buildings account for a significant portion--about 40%--of total energy usage and contribute to around 33% of greenhouse gas emissions [18; 19; 20; 21; 22; 23; 24]. Approximately half of the energy consumption in these buildings connects to HVAC systems, while chillers, which are HVAC applications, account for a substantial portion of this consumption (approximately 25-50%) [12; 25; 26; 27; 28]. Therefore, the predominant expense within the TCO of CWPs is operating cost, which typically falls within the range of 70% to 85% [29]. However, the study by [PERSON] et al. [3] found that operating costs could reach as much as 90% of the TCO. Thus, reducing energy consumption and lowering operational costs would considerably reduce the overall TCO [30; 31; 32]. Forecasting TCO presents significant challenges due to the multiple interrelated parameters and factors exhibiting uncertain, dynamic, and nonlinear characteristics [33; 34]. Dynamic and nonlinear data characteristics refer to when the data do not have clear trends or seasonal patterns [7; 35]. In CWPs, dynamic characteristics could include varying cooling demands throughout the day, changing weather conditions that affect operational efficiency, and fluctuations in energy prices [7]. A nonlinear relationship might be observed between the cooling load and energy consumption [36; 37; 38]. As the load increases, the energy consumption does not necessarily follow linearly due to efficiency gains and operational limits. Moreover, accurately predicting the fluctuation of energy consumption over an extended period, alongside either changing electricity or energy prices, poses considerable challenges [39; 40; 41; 42]. An ideal solution should be capable of learning from historical data and adapting to dynamic and nonlinear system changes [43; 44; 45].