AI-Powered Dashboards: Integrating Visualization and Machine Learning for Enhanced Insights

Modern data analytics is undergoing a paradigm shift with the integration of artificial intelligence into visualization systems. Traditional dashboards primarily focus on descriptive analytics, limiting their ability to provide proactive insights. AI-powered dashboards extend these capabilities by incorporating predictive analytics, anomaly detection, and automated recommendation systems. This paper explores the design, development, and evaluation of intelligent dashboards that leverage machine learning techniques to enhance decision-making processes. The study presents an expanded framework that combines visualization, adaptive interfaces, and AI-driven automation to transform raw data into actionable insights. The proposed approach highlights scalability, real-time processing, and user-centric adaptability as key components of next-generation analytics platforms. Keywords — Artificial Intelligence, Data Visualization, Machine Learning, Predictive Analytics, Smart Dashboards

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-27
DOI
https://doi.org/10.5281/zenodo.19822553
Primary Topic
Data Visualization and Analytics
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Powered Dashboards: Integrating Visualization and Machine Learning for Enhanced Insights

Ved Thote, Raju Gurav, Vishwanath Hiremath, Vibhaw Tripathi
Zenodo (CERN European Organization for Nuclear Research)
Data Visualization and Analytics
article

AI-Powered Dashboards: Integrating Visualization and Machine Learning for Enhanced Insights

Ved Thote, Raju Gurav, Vishwanath Hiremath, Vibhaw Tripathi
article en

Abstract

Modern data analytics is undergoing a paradigm shift with the integration of artificial intelligence into visualization systems. Traditional dashboards primarily focus on descriptive analytics, limiting their ability to provide proactive insights. AI-powered dashboards extend these capabilities by incorporating predictive analytics, anomaly detection, and automated recommendation systems. This paper explores the design, development, and evaluation of intelligent dashboards that leverage machine learning techniques to enhance decision-making processes. The study presents an expanded framework that combines visualization, adaptive interfaces, and AI-driven automation to transform raw data into actionable insights. The proposed approach highlights scalability, real-time processing, and user-centric adaptability as key components of next-generation analytics platforms. Keywords — Artificial Intelligence, Data Visualization, Machine Learning, Predictive Analytics, Smart Dashboards

Zenodo (CERN European Organization for Nuclear Research)
MIT Art, Design and Technology University (IN)
Peace, Justice and strong institutions
Openalex Percentile: Top 4%
Data Visualization and Analytics
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