###### Abstract
Artificial intelligence learns to recognise patterns and structures in datasets before predicting future events based on the model learned. Moreover, ML algorithms can discover specific rules for the system under investigation as it does not rely on user-specified models to analyse big data. ML has been increasingly utilised to analyse big agricultural data, including crop type prediction from satellite data, crop yields, irrigation needs, pest and disease attacks, and weed identification [15; 16]. This approach is adopted in crop yield in crops such as corn [21; 22], grape [23], soybean [24], wheat [25; 26], chilli [27], and paddy [28], and especially in the palm oil industry, which automates various tasks such as tree counting and plant health assessment due to machine learning's incredible learning and significant computational power [29; 30; 31]. Modern agriculture is motivated by examining all factors contributing to crop yield gaps. Nevertheless, various factors threaten agricultural productivity, including biotic and abiotic ones, amplified by climate change, which affects its long-term viability. The yield prediction of tree crops, such as oil palms, is a significantly challenging task. Multilayered and large datasets are required to understand and mitigate these risk factors. Conventional methods are inapplicable to map their connections which explicitly deal with interdependent and erratic elements. Furthermore, advanced analytics must be integrated with the highly heterogeneous datasets, generating insights into the critical constraints of yields at the tree and field scales. Thus, ML serves as an efficient technique to obtain accurate results and appropriate solutions for complex problems. This \"divide and conquer\" ML strategy was used to study the overall yield gap using microcomponents analysis [32; 33]. The studies concluded that investigating individual yield-reducing factors could shed light on the overall impact. Machine learning offers a quantitative perspective in assessing different variables such as soil moisture, rainfall, yield, solar radiation, and plant growth. On the other hand, breeding a bunch offers a qualitative angle on the matter [34; 35; 36]. The preceding statement entails making such research accessible as scientifically structured reviews can help new researchers with information based on accumulated data. The scientific community's strategic concerns can be illustrated, and data from cluster analyses can be assessed using this method. Moreover, researchers can identify research gaps by referring to previously described systems, concepts, and propositions. The review supports an effective research strategy predicated on previous research and current techniques [37]. Multiple studies were conducted on practical tools and ML techniques related to the palm oil industry. For instance, studies investigated remote sensing applications, followed by breeding applications and technologies in monitoring palm oil plantations [38; 39]. Other studies assessed the biofuel-processing technologies in dealing with fruit and palm oil waste [40]. Meanwhile, [PERSON] et al. [41] and [PERSON] et al. [42] investigated ML to detect nutritional deficits in palm oil using proximal images. Studies have also reviewed the ML features in automated fruit grading using image processing and predicting crop yields, including palm oil [43] Nevertheless, most of the current studies did not conduct a thorough literature review. Table 1 lists the objectives of the most recent related review articles, their critical evaluations, and publication dates. Based on Table 1, only several aspects of palm oil using ML are discussed, i.e., yield prediction, crop monitoring, and nutrient deficits. Over the last decade, unidimensional reviews have yet to present the full scope of palm oil cultivation involving ML and multiple artificial intelligence (AI) aspects. Thus, this review article provides an in-depth perspective on how ML is used in palm oil cultivation, summarising everything that has been conducted to date.