The integration of artificial intelligence (AI) in digital pathology is transforming cancer diagnostics by enabling automated histopathological analysis for precise grading and prognostic outcome prediction. Traditional pathology workflows rely on manual microscopic examination of tissue samples, a process that is time-intensive and prone to interobserver variability. AI-driven computational pathology, leveraging deep learning and machine learning algorithms, has demonstrated remarkable accuracy in detecting malignancies, assessing tumor microenvironments, and predicting patient outcomes based on histological features. This study explores the advancements in AI for automated histopathological analysis, focusing on deep convolutional neural networks (CNNs), transformer-based models, and multi-modal learning frameworks. AI models trained on large-scale digitized whole-slide images (WSIs) can identify intricate morphological patterns, quantify tumor heterogeneity, and facilitate objective cancer grading. Additionally, AI-powered prognostic models integrate histopathological data with molecular and clinical information to enhance predictive accuracy for disease progression and treatment response. The study also examines challenges in AI-driven pathology, including domain adaptation, dataset biases, and the need for explainability in clinical decision-making. A comparative analysis of AI-assisted versus pathologist-led cancer grading highlights AI’s potential to enhance diagnostic reproducibility, reduce workload, and improve patient stratification for personalized therapy. Future research directions emphasize federated learning for privacy-preserving pathology AI, real-time WSI processing, and regulatory frameworks for AI adoption in clinical pathology. The integration of AI into digital pathology workflows presents a paradigm shift in oncological diagnostics, paving the way for faster, more reliable, and highly scalable cancer prognostic tools.