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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM48675
Volume:
9 |
5 |
May 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Introduction: The evolution of web development has been driven by technological advancements that have transformed static websites into dynamic, AI-powered platforms. This shift has revolutionized user experiences, business applications, and the way developers build web applications. Objective: This research aims to analyze the progression of web development from its early stages to AI-driven solutions. It seeks to explore the impact of new technologies such as cloud computing, machine learning, and automation on modern web applications. Methodology: A detailed review of academic research, industry reports, and historical case studies was conducted to examine the key milestones in web development. A comparative analysis of static websites, dynamic platforms, and AI-driven applications was used to highlight technological advancements. Additionally, insights from leading web development trends were assessed to predict future developments. Results: Findings indicate that web development has evolved from simple text-based pages to dynamic applications with real-time interactivity. The introduction of server-side scripting, JavaScript frameworks, and cloud computing has led to faster, more scalable solutions. AI-driven features such as personalized recommendations, chatbots, and automated design tools are further shaping the web landscape, improving efficiency and user engagement. Conclusion: Web development continues to evolve, with AI and automation playing a crucial role in shaping future trends. Developers must adapt to these changes to build scalable, intelligent, and secure web applications. The future of web development will be defined by AI integration, enhanced accessibility, and data-driven decision-making.
KEYWORDS
Publication Date
5/28/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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