AI and Machine Learning in Broadcast Media

As digitalization accelerates, television broadcasting is undergoing a profound transformation that extends beyond content production and distribution to include editorial practices and evolving relationships with audiences. Central to this shift is the growing integration of artificial intelligence (AI) and machine learning, which are redefining how broadcast media operates. These technologies enable advanced audience analytics, personalized content recommendations, automated news production, and optimized scheduling, positioning data as a core driver of decision-making in modern broadcasting environments. The rise of digital television platforms and hybrid broadcasting models has further reinforced the strategic role of AI, making it essential to examine how data-driven systems are reshaping both industry practices and broader media frameworks. AI and Machine Learning in Broadcast Media examines how AI and machine learning are transforming television broadcasting from conceptual, technical, and editorial perspectives. The book analyzes the shift from traditional broadcasting to digital and hybrid platforms through AI-driven applications, highlighting changes in content production, scheduling, audience measurement, personalization, and automation. Covering topics such as personalized broadcasting, algorithmic nudging, and audience insight, this book is a fundamental resource for graduate and doctoral students, broadcast professionals, television producers, journalists, data analysts, technology developers, and more.