This 21st century is notable for experiencing so many disturbances at economic, social, cultural, and political levels in the entire world. The outbreak of novel corona virus 2019 (COVID-19) has been treated as a Public Health crisis of global Concern by the World Health Organization (WHO). Various outbreak models for COVID-19 are being utilized by researchers throughout the world to get well-versed decisions and impose significant control measures. Amid the standard methods for COVID-19 worldwide epidemic prediction, easy statistical, as well as epidemiological methods have got more consideration by researchers and authorities. One main difficulty in controlling the spreading of COVID-19 is the inadequacy and lack of medical tests for detecting as well as identifying a solution. To solve this problem, a few statistical-based advances are being enhanced and turn into a partial resolution up-to some level. To deal with the challenges of the medical field, a broad range of intelligent based methods, frameworks, and equipment have been recommended by Machine Learning (ML) and Deep Learning. As ML and DL have the ability of identifying and predicting patterns in complex large datasets, they are recognized as a suitable procedure for producing effective solutions for the diagnosis of COVID-19. In this paper, a perspective research has been conducted in the applicability of intelligent systems such as ML, DL and others in solving COVID-19 related outbreak issues. The main intention behind this study is (i) to understand the importance of intelligent approaches such as ML and DL for COVID-19 pandemic, (ii) discussing the efficiency and impact of these methods in the prognosis of COVID-19, (iii) the growth in the development of type of ML and advanced ML methods for COVID-19 prognosis,(iv) analyzing the impact of data types and the nature of data along with challenges in processing the data for COVID-19,(v) to focus on some future challenges in COVID-19 prognosis to inspire the researchers for innovating and enhancing their knowledge and research on other impacted sectors due to COVID-19.

21世纪是一个值得注意的世纪，全世界在经济、社会、文化和政治层面经历了如此多的动荡。 2019新型冠状病毒（COVID-19）的爆发已被世界卫生组织（WHO）视为全球关注的公共卫生危机。世界各地的研究人员正在利用各种 COVID-19 爆发模型来做出明智的决策并实施重要的控制措施。在COVID-19全球流行病预测的标准方法中，简单的统计方法以及流行病学方法得到了研究人员和当局的更多考虑。控制 COVID-19 传播的一个主要困难是用于检测和确定解决方案的医学测试不足和缺乏。为了解决这个问题，一些基于统计的进展正在得到加强，并在某种程度上转变为部分解决方案。为了应对医疗领域的挑战，机器学习（ML）和深度学习推荐了一系列基于智能的方法、框架和设备。由于 ML 和 DL 能够识别和预测复杂大型数据集中的模式，因此它们被认为是为诊断 COVID-19 提供有效解决方案的合适程序。本文对机器学习、深度学习等智能系统在解决 COVID-19 相关爆发问题中的适用性进行了前瞻性研究。 这项研究的主要目的是 (i) 了解 ML 和 DL 等智能方法对 COVID-19 大流行的重要性，(ii) 讨论这些方法在 COVID-19 预后中的效率和影响，(iii)用于 COVID-19 预测的 ML 类型和高级 ML 方法的发展的增长，(iv) 分析数据类型和数据性质的影响以及处理 COVID-19 数据的挑战，(v) 重点关注关于 COVID-19 预后中的一些未来挑战，以激励研究人员创新并加强他们对受 COVID-19 影响的其他部门的知识和研究。