The motivation behind fuzzy logic in data mining is to address the inherent uncertainty and imprecision in real-world data and make the mined results more interpretable for humans. Temporal Fuzzy High Utility Itemset Mining, which incorporates transaction time, is an emerging field with significant potential for analyzing time-sensitive data. Although several studies in this area have been conducted, for instance, recently fuzzy list-based approaches, a significant challenge remains in joining operations of conditional fuzzy lists when generating candidate itemsets. To solve this, we have proposed a pruning strategy based on item co-occurrences to reduce the number of join operations using anti-monotonic property. Experiments on real datasets show our approach outperforms traditional algorithms in terms of runtime and candidate generations with little memory overhead, up to 95% of non-promising candidates are pruned.