design, and computational infrastructure. Traditional ML algorithms, which were not designed to handle the vast amounts of data generated today, often struggle with the issues of scalability, efficiency, and accuracy. Additionally, the heterogeneous nature of big data--encompassing structured data from databases, unstructured data from text and images, and semi-structured data such as JavaScript object notation (JSON) files--further complicates the task of developing effective ML models [6, 7, 8]. Moreover, integrating ML into big data environments raises critical concerns about model interpretability, data privacy, and ethical considerations. As ML models become more complex, understanding how they arrive at their decisions becomes increasingly difficult, which can be problematic in high-stakes applications such as healthcare and finance. Furthermore, the use of personal and sensitive data in ML models raises significant privacy and security issues, necessitating the development of new techniques to protect individual rights while still enabling the use of large-scale data for analysis [9, 10, 11, 12]. The present survey differentiates itself from the existing literature summarized in Table 1 by providing a more integrative and forward-looking perspective on the intersection of ML and big data. Unlike the surveys in Table 1, which primarily focus on domain-specific applications (e.g., energy, healthcare, and transportation) or challenges (e.g., scalability and interpretability), this survey adopts a broader framework. It systematically identifies critical challenges such as scalability, interpretability, data privacy, and ethical considerations while addressing their interconnections with ML techniques and frameworks. A key differentiation is its focus on the ethical and societal implications of ML in big data, an area often overlooked in the reviewed surveys. Furthermore, this survey uniquely emphasizes the need for sustainable and privacy-preserving ML, proposing advanced \begin{table}
\begin{tabular}{p{42.7 pt} p{284.5 pt}} \hline
**Reference** & **Description** \\ \hline
[13] & It discusses ML techniques for big data, including supervised, unsupervised, and semi-supervised learning, and their applications in fields like healthcare, agriculture, and robotics. It highlights challenges like data imbalance and offers remedies for researchers. \\ \hline
[14] & This survey explores ML methods tailored for big data applications. It addresses features, challenges, and practical uses of algorithms like classification and clustering, emphasizing their scalability and application in domains like healthcare and finance. \\ \hline
[15] & This is a paper providing an overview of ML methods for big data that examines challenges in processing large datasets and highlights promising approaches like distributed and parallel learning. The paper also discusses open research directions. \\ \hline
[16] & This is a survey focusing on big data and ML applications in the electrical power and energy sectors. It reviews challenges, solutions, and opportunities in applying ML for smart grid operations and energy management. \\ \hline
[17] & This paper compiles challenges associated with the use of ML and big data, categorizing them by the dimensions of big data (volume, velocity, variety, and veracity). It reviews emerging techniques and solutions, offering insights into research gaps and future directions. \\ \hline
[18] & This paper provides a detailed literature survey on ML for big data processing, covering techniques like deep learning (DL), representation learning, and their integration with signal processing. It highlights challenges like high dimensionality and discusses potential research trends. \\ \hline
[19] & This survey provides a discussion of opportunities and challenges associated with ML and big data. It introduces a framework that integrates ML phases (preprocessing, learning, and evaluation) with components like user input, the domain, and system architecture. \\ \hline \end{tabular}
\end{table}