This paper presents a new combinatorial algorithm for the classic Correlation Clustering problem that achieves a 1.847-approximation factor, drastically improving over the previous best 3-approximation. The algorithm runs in sublinear time and space and uses only a constant number of rounds, making it highly efficient and practical.