<![CDATA[The rapid growth of data volume, dimensionality, and heterogeneity has challenged the effectiveness of conventional machine learning models, which were originally designed for smaller and more homogeneous datasets. This study analyzes the structural and computational limitations of traditional models such as Logistic Regression, Naïve Bayes, Decision Trees, and Support Vector Machines in handling large-scale and diverse data. Using a combination of literature review, experimental evaluation, and comparative analysis, the research investigates how these models perform under increasing data size, varying feature complexity, and mixed data modalities. Key performance metrics, including accuracy degradation, training time escalation, memory consumption, and scalability constraints, are examined to identify critical thresholds where conventional techniques begin to fail. The results show that traditional models exhibit significant performance drops, resource saturation, and reduced robustness when faced with high-dimensional or heterogeneous datasets, particularly in comparison to modern deep learning and distributed learning approaches. These findings align with earlier theoretical studies but provide new empirical evidence that quantifies failure points and broadens the understanding of scalability limitations. The study concludes that while classical machine learning approaches remain effective for small and structured datasets, they are increasingly unsuitable for contemporary data-intensive environments. This research highlights the necessity of transitioning toward more scalable, adaptive, and representation-rich models to meet current and future data challenges.]]>
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