Artificial Intelligence (AI) has evolved remarkably over the past seven decades, transforming from simple rule-based systems into complex multimodal and generative frameworks capable of reasoning, creativity, and perception. This review traces the chronological development of AI tools, highlighting key milestones that shaped the field—from the early symbolic programs like Logic Theorist and ELIZA to the emergence of modern large-scale models such as GPT-4, Gemini, and Claude. The study explores the progression across distinct eras: the foundational period of symbolic reasoning (1940s–1970s), the rise of machine learning and statistical modeling (1980s–2000s), the deep learning revolution (2010s), and the recent explosion of generative and multimodal systems (2020–2025). Each phase reflects a major shift in how intelligence is defined, represented, and implemented—from handcrafted logic to data-driven learning and now to context-aware multimodal understanding. By reviewing over fifty significant AI tools and frameworks, this paper provides a comprehensive overview of how incremental innovations in computation, data availability, and model architecture have collectively enabled the current state of AI. The work concludes with insights on how this evolution paves the way for the next generation of agentic and real-time AI systems capable of seamless interaction across text, image, audio, and video modalities.

The rapid evolution of cyber threats has necessitated increasingly sophisticated detection methodologies that transcend traditional signature-based approaches. In recent years, Convolutional Neural Networks (CNNs) and multi-modal deep learning architectures have emerged as powerful paradigms for addressing diverse cybersecurity challenges, ranging from malware detection and phishing identification to network intrusion detection and encrypted traffic classification. This paper presents a comprehensive review of 50 recent research works (20222025) that leverage CNN-based and multi-modal learning techniques in the cybersecurity domain. We systematically categorize these approaches into seven distinct groups: (1)~CNN-based malware visualization and detection, (2)~CNN-based phishing and web threat detection, (3)~multi-modal and hybrid intrusion detection systems, (4)~CNN-based IoT and botnet detection, (5)~encrypted and darknet traffic classification, (6)~adversarial and GAN-based cybersecurity methods, and (7)~multi-modal cybersecurity applications. For each category, we analyze core methodologies, architectural innovations, and performance benchmarks. Furthermore, we provide detailed comparisons of datasets, evaluation metrics, and identify critical gaps in current research. Our analysis reveals that while CNN-based visualization achieves remarkable accuracy ($>$98\%) in controlled environments, significant challenges remain in adversarial robustness, cross-domain generalization, real-time deployment, and the effective fusion of heterogeneous data modalities. We conclude by outlining open challenges and future research directions that can advance the field toward more resilient, adaptive, and deployable cybersecurity systems.