• Rehan Syed
• Suvarna D. Pingle

The rapid growth of big data applications and decentralized networks has intensified the need for efficient, scalable, and adaptive search algorithms. Traditional blind search methods such as Flooding (BFS-based) and Random Walk (DFS-based) suffer from limitations involving high network overhead, long search cycles, or low accuracy. The Hybrid Breadth‑Depth Algorithm (HBDA) integrates correlation‑based routing, combining BFS and DFS logic to optimize success rate, reduce message transmission, and improve matching accuracy. This review summarizes existing approaches, evaluates HBDA’s performance, examines its tools and methodology, and highlights future advancements in intelligent search within large‑scale unstructured networks

Intelligent Search Algorithms, Big Data, Hybrid Breadth–Depth Algorithm, Correlation-Based Routing, Unstructured Networks, Distributed Search

"A REVIEW ON INTELLIGENT SEARCH ALGORITHM FOR BIG DATA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 11, page no.h40-h48, November-2025, Available :http://www.jetir.org/papers/JETIR2511716.pdf

"A REVIEW ON INTELLIGENT SEARCH ALGORITHM FOR BIG DATA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 11, page no. pph40-h48, November-2025, Available at : http://www.jetir.org/papers/JETIR2511716.pdf