Wireless MIMO communication systems nowadays are driven by the requirement to minimize the hardware computational complexity of detection algorithms while preserving high detection performance. To address this challenge, traditional tree-based approaches like the K-best algorithm have been proposed, which employ a fixed complexity at each layer to manage computational demands. The K-best algorithm remains computationally intensive due to its complexity highly dependent on the QAM modulation size and is further followed by a sorting scheme applied at each layer. Another tree-based approach, called the box decoding, has been developed to overcome these limitations in small-scale MIMO systems. However, the complexity of this algorithm escalates significantly in MIMO systems with higher dimension as the number of candidates generated by the box decoding grows substantially. In this paper, we propose an innovative solution, called the K-box algorithm. In contrast to K-best, K-box decouples the candidate selection procedure and the size of constellation space similar to box decoding while restraining the candidates expansion in higher-dimensional MIMO by sorting only when the number of candidates exceed a certain limit. Testing on 4 × 4 and 8 × 8 64-QAM systems for 5G new radio (NR) link demonstrates SNR gains of 0.6 dB at a BER of 10−2 compared to the K-best algorithm, while achieving 77% and 76% complexity reduction in partial Euclidean distance (PED) computations and sorting complexity reductions of 94% and 92% respectively.